Biochar Unveiled: Advanced Investigation

**Chapter 14**

## Biochar and Application of Machine Learning: A Review

*Kingsley Ukoba and Tien-Chien Jen*

#### **Abstract**

This study discusses biochar and machine learning application. Concept of biochar, machine learning and different machine learning algorithms used for predicting adsorption onto biochar were examined. Pyrolysis is used to produce biochar from organic materials. Agricultural wastes are burnt in regulated conditions to produce charcoal-like biochar using pyrolysis. Biochar plays a major role in removing heavy metals. Biochar is eco-friendly, inexpensive and effective. Increasing interest in biochar is due to stable carbon skeleton because of ease of sourcing the precursor feedstock and peculiar physicochemical. However, artificial intelligence is a process of training computers to mimic and perform duties human. Artificial intelligence aims to enable computers to solve human challenges and task like humans. A branch of artificial intelligence that teaches machine to perform and predict task using previous data is known as machine learning. It uses parameters called algorithms that convert previous data (input) to forecast new solution. Algorithms that have been used in biochar applications are examined. It was discovered that neural networks, eXtreme Gradient Boosting algorithm and random forest for constructing and evaluating the predictive models of adsorption onto biochar have all been used for biochar application. Machine learning prevents waste, reduces time and reduces cost. It also permits an interdisciplinary means of removing heavy metals.

**Keywords:** review, machine learning, biochar, AI, adsorption

#### **1. Introduction**

The world is embracing the fourth industrial revolution and adapting technology in every sphere of human endeavours. 4IR is adjusting ways humans engage, work and live [1]. Its ushers humanity into a new phase caused by incredible technological advancements comparable to the first, second and third industrial revolutions. Machine learning has been deployed simply in different aspects of human lives to living and cost [2, 3]. It is gaining interest in biochar. Biochar is a produced using pyrolysis. Forestry and agricultural wastes are burnt in regulated conditions to produce biochar [2, 3]. This study examines the various algorithms used in machine learning to predict adsorption in biochar.

Fourth Industrial Revolution will alter patterns of key sectors. This includes technological shift, deviation in societal patterns and processes caused by increased interconnection among other features [4]. It hopes to transform the ways things are done. Things will communicate via networks, data sharing and the likes. It is an era that will see machines perform tasks more than before. The machines will learn using previously generated data and transform those learning to solve human challenges. This is all-encompassing, including in biochar.

Biomass conversion without oxygen produces a solid product (biochar) [5–7]. Stability of biochar is responsible for carbon sequestration [8]. It could be a way to combat climate change [9, 10]. Biochar improves soil fertility. It increases agricultural yield in acidic soils [11, 12]. Biochar is made from various organic waste feedstocks, including agricultural waste and sewage sludge [13, 14]. Biochar has many applications, including heat and power generation and a soil amendment. Process parameters and feedstock influence the characteristics of carbonised biomass. Selection of acceptable conditions to manufacture a char with the necessary qualities thus necessitates quantitative and qualitative knowledge of interdependence and affecting factors [15].

In machine learning, input is a set of instructions (algorithms) used to generate result. It learns from previous data to perform and optimise operations. Attempts have been made to adapt machine learning in biochar [16, 17].

There have been attempts to implement machine learning in various aspects of biochar [18], review machine learning [19, 20] and review biochar [21]. However, there is limited literature focusing on the review of machine learning in biochar. This forms the basis of this study. The concept of biochar is examined and, after that, machine learning. This is closely followed by examining biochar and machine learning.

#### **2. Biochar: history, properties and applications**

#### **2.1 History of biochar**

The term 'biochar' is a late-twentieth-century English neologism. It is from a Greek words 'o, bios' or 'life' and 'char' or 'clarification' (charcoal produced by carbonisation of biomass) [22]. It is charcoal, prevalent in soil, aquatic ecosystems and animal digestive systems and participates in biological processes. Biochar usage for soil nutrient retention and improvement started in the Brazilian Amazon about 2000 years ago [23]. John Miedema, a commercial fisherman, organic farmer and inventor, first learned about biochar 5 years ago while looking for a better solution to clean up effluent from a dairy manure digester [24]. Biochar was made by pre-Columbian Amazonians by covering burning biomass with soil in ditches [25]. Terra preta de Indio was the name given to it by European settlers [26].

#### **2.2 Production of biochar**

Biochar is made by heating biomass without oxygen, either completely or partially [27, 28]. The most common process for making biochar is pyrolysis, which can also be found in the early stages of gasification ad combustion [29]. Biochar is made from different biomass sources, including solid wastes, plant materials, biomass from wood, agricultural residues and so on [30, 31]. Pyrolysis is a typical technique to produce

biochar. The process is performed between 400 and 1000°C [32, 33]. Pyrolysis, hydrothermal carbonisation, gasification, flascarbonisation and torrefaction are some of the most prevalent thermochemical processes used to make biochar [34–36]. Pyrolysis is the most common biochar production method of all of these [37]. The process is depicted in Eq. (1).

$$\rm{C}\_{6}\rm{H}\_{8.7}\rm{O}\_{4} \rightarrow \rm{C}\_{3}\rm{H}\_{1.4}\rm{O}\_{0.4} \tag{1}$$

Biomass Biochar →

Biochar is created the same way as charcoal, but it is meant to be used as an adsorbent and a soil amendment [38]. The end use of the material is, in essence, the key. If it is meant to be used as a fuel, it is called charcoal, and it is made with the best fuel qualities possible.

#### **2.3 Properties of biochar**

Biochar's efficacy as a soil amendment is influenced by its chemical and physical qualities. As biochar interacts with bacteria, mineral substances and soil organic and plant roots, its characteristics alter. The biochar qualities affect its performance as a soil amendment.

Biochar comes in various forms, each with its own set of characteristics. Biochar's qualities impact how well it works as a soil amendment [39]. It can be altered by conditioning, which includes adding minerals, nutrients and/or microorganisms to the biochar after it has been made [40]. Biochar from clean biomass differs from biochar produced with field residue in terms of the qualities. This is because the field residue biochar has been mixed with fertilisers, soil and manure. The characteristics of biochar are altered when it is mixed with soil organic, mineral substances and bacteria. Biochar improves with age.

Biochar properties are influenced by the type of biomass used [41]. As long as the biomass is not polluted with hazardous compounds, it can be used to make biochar (e.g. heavy metals, PCBs). Biochar feedstocks include plant residues, grasses, industrial wastes, woods, seaweed, manures, MSW, food waste [42]. **Figure 1a** shows the pyrolysis of seaweed to produce a biochar. **Figure 1b** shows the evolution of biochar from biomass.

The properties of biochar are grouped under chemical and physical [45] in **Table 1**.

**Figure 1.** *(a) Process of seaweed pyrolysis to biochar [43] and (b) biomass to biochar [44].*


#### **Table 1.**

*Summary of biochar properties.*

#### *2.3.1 Physical properties of biochar*

Biochar's physical features influence its environmental mobility, interactions with minerals, soil water, nutrients and usefulness as an ecological niche for soil microorganisms and mycorrhizal fungus by soil microorganisms mycorrhizal fungus providing surfaces, growing space and predator protection [46]. Physical parameters such as particle density and size, porosity, bulk density and surface area are numerical and action connected. Porosity affects particle density and surface area [47]. Biochar with high porosity and low density may hold more water. However, wind and water easily remove such biochar. The quality of biochar is affected by heating rate, biomass type [48] as enumerated in **Figure 2**.

Grass biochar has a particle density of 0.25–0.3 g/cm3 , while wood biochar has 0.47–0.6 g/cm3 [49]. Particle density of biochar affects the loss and movement in water or wind [50]. Biochar with a low bulk density can be used to remediate wall gardens and compacted soils. Pore sizes can vary by six orders of magnitude and are classed as macro-, meso- and micro-pores, with varied implications for biochar interactions with the environment [51, 52]. Most woody biochar has low bulk densities, medium-to-high surface area and porosity [53, 54]. The process utilised to make biochar has an impact on porosity.

Hydrophobicity impacts biochar's water uptake, its water holding capacity and microbial interactions. Tars (aliphatic chemicals) condensing on the charcoal surface during pyrolysis induce hydrophobicity. Biochar has high hydrophobic at low temperatures. However, longer pyrolysis times can lessen hydrophobicity. Hydrophobicity may diminish as biochar mixes with soil.

A low Hardgrove Grindability Index (HGI) indicates that the material is difficult to grind, whereas a high HGI value suggests that the material is easy to grind [55, 56]. HGI of 80–120 can be achieved for woody biochar having volatile matter content of about 20%, which is commonly achieved at temperatures around 600°C, defining charcoal as easily grindable.

#### *2.3.2 Chemical properties of biochar*

Persistent carbon is composed of carbon ring structures, with some nitrogen and oxygen thrown in. Structures' ring sizes are determined by temperature of biochar production. Biochars' water-soluble and mineralisable chemicals can nourish bacteria and can boost seeds and plant nutrient and yield. Water-extractable organics are substantially more abundant in low-temperature biochars. Total and bioavailable polycyclic aromatic hydrocarbons (PAH) have maximum acceptable limits. A common (90%) PAH in biochar is naphthalene. Many biochars at 350–500°C have included mineralisable organic molecules that benefit plants and soil [57, 58]. Low dosages of

*Biochar and Application of Machine Learning: A Review DOI: http://dx.doi.org/10.5772/intechopen.108024*

**Figure 2.** *Factors affecting biochar quality.*

phenols, butenolide (a component of tobacco), carboxylic and fatty acids and even PAH can encourage plant development. In contrast, high quantities can inhibit or kill it, a phenomenon known as hormesis.

#### **2.4 Merit and demerit of biochar**

Biochar continues to attract interest owing to its vast potential and benefit. However, there are some disadvantages associated with it. Discussed below are the merit and demerit of biochar.

#### *2.4.1 Merit of biochar*

Biochar is a carbon-rich substance, some scientists believe that it is the secret to soil renewal [59]. Biochar, which is relatively light and porous, can act as a sponge and provide a home for various beneficial soil microbes useful for soil and plant health. It increases agricultural production. Biochar can remove CO2 from the atmosphere for long periods and provide other environmental benefits [60]. Plants transform carbon dioxide from the air into organic material, or biomass, through photosynthesis. It helps in climate change mitigation [10].

#### *2.4.2 Demerit of biochar*

It absorbs nutrients, resulting in a nutrient deficit in growing plants [47, 61]. Biochar application regularly creates soil compaction, which reduces crop yield. Land loss is also due to erosion, pollution risk, agricultural residue removal and worm life rate reduction.

#### **2.5 Application of biochar**

Biochar is useful in several applications [62]. It is used to enhance soil health via soil amendment. It also serves as microbial carrier immobilising agents for remediation of toxic metal and organic contaminant in water and soil. It is catalyst for industrial application, porous materials for mitigating greenhouse gas emission and odorous compound. It is used as feed supplements to improve nutrient intake efficiency, animal health and hence productivity [63]. **Figure 3** shows the influence of biochar properties on the agriculture and soil conditions.

#### *2.5.1 Biochar for soil amendment*

Biochar has a lot of potential as a long-term product for improving agricultural soil health and fertility. The manufacture of biochar and its impact on soils can help to reduce the need for commercial fertilisers. Diverse research has also reported that addition of biochar to agricultural soil can aid in reducing greenhouse gas emission [64–67].

Biochar is utilised as an agricultural soil amendment because it has a lot of fascinating properties, such as high carbon content, a high pH, high stability, a high porosity and a high surface area [68, 69]. Over the last few years, multiple research studies have been conducted to analyse the global impact of biochar on diverse agricultural soils [70, 71]. Biochar has improved soil's chemical, physical and biological qualities, enhancing crop productivity [72, 73]. Furthermore, biochars with a high surface can be utilised as soil remediation technique to adsorb both inorganic and organic contaminants, for instance, heavy metals, and pesticides, hence minimising leaching into waterway. Once applied to carbon in biochar, soils, that are highly stable, can be sequestered for more than 1000 years.

#### *2.5.1.1 Application of biochar for soil amendment*

When utilised as soil amendments, biochar is incorporated into the plant's root zone – the area of soil surrounding a plant's roots – ideally into 4–6 inches of soil depth. Increasing the time nutrients stay in the soil by mixing up to one part compost with one part biochar, most gardeners start with a ratio of 10 parts compost to one part biochar to ensure that plants tolerate it well.

Several materials such as green waste [74], rice straw [75], poultry litter [76] and other materials have been used for producing biochar using vacuum pyrolysed and other methods of biochar for soil amendments [77].

#### *2.5.2 Carbon sink*

A carbon sink is any natural or artificial reservoir that indefinitely gathers and stores carbon-containing chemical compounds [78]. Also, anything that absorbs more *Biochar and Application of Machine Learning: A Review DOI: http://dx.doi.org/10.5772/intechopen.108024*

carbon from the atmosphere than it releases, such as plants, the ocean and soil, is a carbon sink. Oceans are the primary natural carbon sinks, absorbing over half of all carbon released [79]. Carbon dioxide is sucked from the atmosphere by plants for use in photosynthesis. On the other hand, a carbon source is anything that releases more carbon into the atmosphere than it absorbs, such as fossil fuel combustion or volcanic eruptions [80]. Carbon is deposited on our planet in four major sinks: (1) organic molecules in living and dead organisms in the biosphere; (2) carbon dioxide in the atmosphere; (3) organic matter in soils; and (4) fossil fuels and sedimentary rock deposits such as limestone and dolomite in the lithosphere. Because the process takes a supposedly carbon-neutral phase of naturally decaying, biochar reduces CO2 in the environment.

Growing plants or collecting waste biomass, converting it to biochar and adding it to soils remove carbon dioxide (CO2) from the environment: plants growth eliminates CO2 from the atmosphere and produces additional biomass; the carbon in that biomass is transformed into a stable form [81, 82]. Biochar production can offset about 12% of world's greenhouse gas emissions. At \$30–120 per ton of CO2, biochar might sequester 0.5–2 GtCO2 per year by 2050 [83, 84]. According to the scholarly literature, sequestration rates range from 1 to 35 GtCO2 each year, with a potential of 78–477 GtCO2 in this century [85, 86].

#### *2.5.3 Biochar for water retention*

Water retention refers to how much water a soil can keep for its crops, allowing plants to have more water available. Biochar can improve the soil's water retention and holding ability due to its porous structure. An agriculturally applicable biochar amendment of 5% biochar (approximately 100 metric tons/ha) leads to a 24% increase in water retention capacity over unamended soil or a 50% increase [87]. Researchers have understudied the impact of biochar on water retention [88], on sandy soil [89], clay [90], the application in different agricultural soil [91] and the relationship between plant and water [92]. There has also been the study of southeastern coastal soil [93] and midwestern agricultural soil [94].

#### *2.5.4 Biochar for stock fodder*

Stock fodder, also known as provender, is an agricultural feed used to feed domesticated animals such as cattle, rabbits, sheep and horses [95]. Fodder crops are divided into two categories: temporary and permanent. Fodder is used to describe the crops gathered and utilised for stall feeding. Forage is a vegetative matter used as animal feed, whether fresh or stored. Grasses, legumes, crucifers and other forage crops are farmed and utilised as hay, grazing, fodder and silage.

Xie et al. [96] provided a thorough investigation of biochar's technical features and possible applications as an engineered material for environmental remediation. Mandal et al. [97] presented quantitative data and discussed the benefits of biochar composites over pure biochar. The synthesis of nano-metal-aided biochar and its features and applications in soil improvement and heavy metal removal are discussed. Shakoor et al. [98] discuss how to boost biochar's heavy metal sorption capability by activating it with steam or acids/bases and impregnating biochar-based composite with mineral, organic compound and carbon-rich material. Biochars' chemical/physical activation of biochar can improve their surface area, resulting in better functionality, while pretreatment/modification techniques aid in developing new sorbent

with efficient surface attribute for heavy metal removal from aqueous solution using biochar as a supporting media. This is essential because heavy metal sorption is driven by type of biochar, heavy metal species and various processes, including physical binding, complexation, ion exchange, surface precipitation and electrostatic interactions. Efforts were also made to review the application of biochar to remove heavy metals and toxic elements in water and wastewater [99, 100].

#### **2.6 Future outlook of biochar**

Wood-based biochar is the most popular product, accounting for approximately 64% of the market. Soil conditioner is the most popular application, accounting for almost 82% of the market.

The global biochar market is expected to be worth USD 314.6 million in 2022, with a readjusted size of USD 524.7 million by 2028, representing an 8.9% CAGR (compound annual growth rate) over the research period. From 2021 to 2030, the global biochar markets are expected to increase at a CAGR of 13.2%, from \$170.9 million in 2020 to \$587.7 million in 2030. Carbon Gold, The Biochar Company (TBC), Biochar Supreme, Cool Planet, Black Carbon and Swiss Biochar GmbH, among others, are global biochar significant players. The top three firms account for roughly 20% of the market [101].

#### **3. Machine learning: history, algorithm and application**

Machine learning (ML) is a process of predicting values using a previous learning. It is a subset of AI. It uses set of instructions called algorithm. ML uses algorithm to emulate variable or humanity. AI is used to solve complex tasks like how humans solve problems. There are four types of algorithms. They are reinforcement, unsupervised, semi-supervised and supervised. Python, Java, C++, R and JavaScript are among the top five programming languages and libraries for machine learning. Python is the language of choice for machine learning engineers, with more than 60% of them adopting and prioritising it for development since it is simple to learn. A little coding knowledge is required for the effective deployment of machine learning.

#### **3.1 History of machine learning**

An American IBMer (Arthur Samuel) was first to use machine learning in 1959 [102, 103]. Another term used is 'self-teaching computer' [104, 105]. A book on machine learning for pattern categorisation by Nilsson dominated the1960s [106]. Pattern recognition continued till the 1970s [107]. An approach for teaching neural network using 40 character recognition by computer terminal was documented in 1981 [108, 109]. This terminal included 4 special symbols, 26 letters and 10 digits. Tom Mitchell opined 'A computer program is said to learn from experience E for some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E'. This became accepted machine learning definition [110, 111]. However, the definition provided operational description of the ML tasks instead of cognitive. It aligns with Alan Turing's method 'Computing Machinery and Intelligence', replacing 'Can machine think' with 'Can machines do what we (as thinking creatures) can achieve' [112].

The goal of modern ML is to classify data using standard models and generate predictions about future outcomes using these models. A stock trading machine learning system may provide the trader with future prospective predictions [113, 114].

#### **3.2 Theory of machine learning**

Most beginners' main goal is to generalise what they have learned [115]. Generalisation is ML ability to execute precisely, previously unseen data using algorithm. Data (training) originate from new probability distribution. It represents space of occurrences. Optimisation prediction requires general model development. Computational learning theory is analysis of performance of algorithms. Training sets are limited because of future uncertainty. Learning theory rarely provides guarantees about algorithm performance. Probabilistic performance bounds are tremendously widespread. Bias-variance decomposition is used for generalisation error.

For the best generalisation outcomes, the hypothesis' complexity needs reflect the intricacy of the functions behind the data. If the assumption is fewer intricate than the functions, the system will under-fit the data. Increment in the complexity of the model reduces training error. Poor generalisation due to overfitting is caused by complicated hypothesis of model [116]. Learning theorists look at the temporal intricacy and feasibility of learning in addition to performance bounds. A computation is deemed viable in computational learning theory if it can be completed in polynomial time [117].

#### **3.3 Classification of machine learning approach**

ML is classified as reinforcement, unsupervised and supervised based on feedback or signal as depicted in **Figure 4** [118, 119].

Optimisation problem is solved using reinforced and unsupervised learning [118–120]. Although, supervised learning uses trained labelled data to produce result [121, 122]. Unsupervised learning uses unguided structure to solve problem [123]. Unsupervised learning is either intended or a means to an end (finding hidden patterns in data) (feature learning). It is used to obtain hidden pattern or future learning. Reinforcement learning is the third type. It is interaction in a dynamic circumstance. An example is driving on the road on the computer. Another example is engaging an opponent in competitive game [124]. Incentives (data) are fed to the software to help solve problem.

Unsupervised learning exposes latent patterns and structures from unlabelled data. Supervised learning solves problem using guided learning [125]. **Figure 5** depicts the most often used supervised algorithms.

Deep learning is used to clean heavy metal by constructing improved adsorption models. Machine learning or deep learning can develop models depending on data complexity, dimensionality and end use [127]. However, challenges of complexity and dimensionality are improved by deep learning with encoder.

#### **3.4 Models of machine learning**

Machine learning entails building a model that has been guided by training data. It can subsequently process more data to produce prediction. For machine learning systems, different models have been utilised and investigated. These are shown in **Figure 6**. The models include artificial neural networks, decision trees,

**Figure 4.** *Classification of machine learning.*

#### **Figure 5.**

*Flowchart of supervised machine learning procedure [126].*

support-vector machines, regression analysis, genetic algorithms, Bayesian networks, training models and federated learning [129–131].

The following models have been used in biochar applications. An overview is given for understanding the models.

i.Artificial Neural Networks (ANN) have become increasingly popular [132, 133]. ANN mimics the human brain with parallel processing to develop complex relationship between independent and dependent variables by developing structures for the model training via experimental data and the tool forming pattern between output and input data. It is a great tool because of its benefits in non-linear system adaptations and approximation without knowing the variables' relationship and ease of use [134].

*Biochar and Application of Machine Learning: A Review DOI: http://dx.doi.org/10.5772/intechopen.108024*

ii.Random forest (RF) models are machine learning models that use the results of a series of regression decision trees to predict the output. Each tree is built independently and is based on a random vector sampled from the input data, with the same distribution across the forest. Using bootstrap aggregation and random feature selection, the predictions from the forests are averaged [135]. RF models are reliable predictors for small sample numbers and high-dimensional data. The RF classifier is an ensemble approach for training several decision trees parallel with bootstrapping and aggregation, often known as bagging [136].

#### iii.Support-vector machine

A support-vector machine (SVM) is a supervised machine learning model that uses classification techniques [137]. SVM models can categorise new text after being given sets of labelled training data for each category. Though we might also argue regression difficulties, categorisation is the best fit. The SVM algorithm aims to find the optimum line or decision boundary for categorising n-dimensional space into classes so that additional data points can be readily placed in the correct category in the future [138, 139]. A hyperplane is a name for the optimal choice boundary. The goal of the SVM algorithm is to find a hyperplane in an N-dimensional space that categorises data points. In SVM, a kernel is a function that aids in problem-solving. They give shortcuts to help avoid doing complicated mathematics. The amazing thing about kernel is that it allows us to go to higher dimensions and execute smooth calculations. Kernels allow us to go up to an infinite number of dimensions. SVM is used for regression and classification of problems. It is a linear model. It can solve both linear and nonlinear problems and is useful for a wide range of applications.

C is a hypermeter that is set before the training model to control error, and Gamma is another hypermeter that is placed before the training model to give the decision boundary curvature weight.

#### iv. eXtreme Gradient Boosting Model

Gradient boosting is a machine learning technique used for various applications, including regression and classification [140, 141]. Extreme Gradient Boosting (XGBoost) is an open-source package that implements the gradient boosting technique efficiently and effectively. Extreme Gradient Boosting is a tree-based method that belongs to Machine Learning's supervised branch. It's a machine-learning algorithm that can predict classification or regression. It returns a prediction model in the form of an ensemble of weak prediction models, most commonly decision trees [142].

#### **3.5 Applications of machine learning**

The following are some machine learning applications. Image and speech recognition, traffic prediction, self-driving cars, product recommendation, online fraud detection, stock market trading, medical diagnosis, automatic language translation, email spam and malware filtering, Alexa, Google assistant and Google Maps [119].

#### *3.5.1 Image recognition*

Image recognition is one of the most common machine learning applications [143]. It's utilised in identifying things such as people, places and digital photograph. Automatic buddy tag suggestion is a commonly used facial identification and picture recognition. Facebook has tools that suggest friends auto-tagging. When we submit photos with our friends Facebook, we obtain automatic tags recommended with their names powered by machine learning's face identification and algorithm recognition. It is based on the 'Deep Facia' Facebook projects that manage face recognition and individual identification in photos.

#### *3.5.2 Speech recognition*

The user of Google has the option to 'Search by voice', which falls under recognition of speech and is a prominent machine learning application. Recognition of speech, frequently referred to as 'Computer speech recognition' or 'Speech to text', is the turning process of voice instruction to text. Machine learning technique is now used widely in speech recognition application [144]. Technology of speech recognition is utilised by Alexa, Google Assistant, Siri, and Cortana to obey voice command.

#### *3.5.3 Google Maps is used when visiting a new location or using an app hailing taxi*

The map provides the best route with the shortest routes and forecasts traffic condition. It utilises two techniques in anticipating traffic condition, such as whether traffic is clear, extremely congested or sluggish moving: The vehicle's location is tracked in real time via the Google Map app and sensor. At the same time, the average time has been taken on previous days. Everyone making use of Google Maps contributes to the improvement of the apps. It collects data from the users and transmits it back to the database to improve its performance.

#### *3.5.4 Product suggestions*

Different entertainment and e-commerce organisations, for instance, Netflix, Amazon and others use machine learning to make products recommendation to user. We begin to receive advertisements for the same goods while browsing the internet on the same browser, because of machine learning, whenever we look for a product on Amazon [145]. Google deduces the user's interests and recommends products based on those interests using multiple machine learning techniques. Likewise, when we use Netflix, we receive recommendations for series of entertainment, movies and other contents, which is also based on machine learning.

#### *3.5.5 Self-driving automobiles*

Self-driving cars are one of the most intriguing machine learning applications [146]. In self-driving automobile, machine learning plays key roles. Tesla, the well-known automobile manufacturer, is developing self-driving vehicles. It trains automobile model to recognise people and object while driving using an unsupervised learning method.

#### *3.5.6 Medical diagnosis*

In medical science, machine learning is used to diagnose disorders [147, 148]. Therefore, medical technology is evolving rapidly, and 3D model that can predict the exact lesions location in the brain is now possible. It facilitates the brain cancers detection and other brain-related illness.

#### *3.5.7 Automatic language translation*

Machine learning aids in translation by transforming text into familiar language. This feature is provided by Google Neural Machine Translation (Google's GNMT), a Neural Machine Learning that translates text into native language automatically. Sequence-to-sequence learning methods are the technology behind automatic translation, coupled with translation of text from one language to another and picture recognition.

#### **3.6 Limitations of machine learning**

Machine learning has proved transformative in several domains, yet it frequently fails to produce the promised outcomes [149]. There are various reasons for this, including a lack of (appropriate) data, data access issues, data bias, privacy issues, poorly designed tasks and algorithms, incorrect tools and personnel, a lack of resources and evaluation issues [150]. In 2018, an Uber self-driving car failed to identify a person, and the pedestrian (Elaine Herzberg) was killed due to the incident [151, 152]. Even after years of effort and billions of dollars, IBM Watson's attempts to employ machine learning in healthcare failed to deliver [153]. Machine learning has been utilised in updating evidence concerning systematic reviews and increased reviewer concerns due to the biomedical literature development. When students 'learn the wrong lesson', they can be disappointed. An image classifier trained just on photographs of brown horses and black cats, for example, may conclude that all brown patches are most likely horses [106]. In the real world, unlike people, existing

image classifiers frequently do not make decisions based on the spatial relationships between picture component and instead study associations between pixels that human is unaware of but correlates with specific sorts of image of real object. Modifying this pattern on lawful images can cause the algorithm to misclassify the image as 'adversarial' non-linear systems, or non-pattern disturbances can potentially lead to adversarial vulnerabilities. Several systems are so fragile that single change hostile pixel causes misclassification.

#### **3.7 Ethics of machine learning**

Machine ethics (also known as machine morality, computational morality or computational ethics) is a branch of artificial intelligence ethics concerned with enhancing or ensuring the moral behaviour of man-made machines that employ artificial intelligence, also known as artificial intelligent agents [154, 155]. Privacy and surveillance, bias and discrimination and perhaps the deepest, most difficult philosophical question of the era, the role of human judgement, are three major ethical concerns for society, according to Sandel, who teaches a course on the moral, social and political implications of new technologies [156, 157].

#### **3.8 Hardware of machine learning**

More effective techniques in training deep neural network (machine learning specific subdomain) that incorporate various non-linear hidden unit layers have been developed since the 2010s, thanks to developments in computer technology and machine learning algorithms [158]. By 2019, GPUs had supplanted CPUs as the most common way of training large-scale commercial cloud AI, frequently with AI-specific upgrades [159]. From AlexNet (2012) to AlphaZero (2017), OpenAI calculated the amount of hardware computing required in large deep learning project and discovered 300,000-fold increase in the required computing amount, with 3.4-month doubling-time trendline [160].

There are embedded machine learning and neuromorphic or physical neural networks.

#### *3.8.1 A physical neural network*

A physical neural network also known as a neuromorphic computer, is an artificial neural network in which an electrical changeable substance emulates the neural synapse function. The term 'physical' neural network refers to physical hardware to simulate neurons rather than software-based techniques. Other artificial neural networks that use memristor or other electrical adjustable resistance materials to imitate neural synapse are also known as memristor networks [161, 162].

#### *3.8.2 Embedded machine learning*

Embedded Machine Learning is a sub-field of machine learning that uses embedded system with low computing capabilities, for instance, microcontrollers, wearable computers and edge devices to run machine learning models. Running machine learning models in embedded device eliminates the necessity to transport and store data on cloud server for processing further, resulting in fewer data breach and privacy leak and less theft of intellectual property, personal data and company trading secrets.

Embedded Machine Learning can be implemented using various methods, including hardware acceleration, approximation computation and machine learning model optimisation [163].

#### **3.9 Software of machine learning**

Different software suites having various algorithms have been used for machine learning. Some are free and open-source, and others are proprietary. The open-source and free software includes Caffe, ELKI, Deeplearning4j, Microsoft Cognitive Toolkit and DeepSpeed. However, KNIME and RapidMiner are the most popular open-source proprietary software [164], alongside R tool and Weka [165]. R tool is free and used for environmental statistics. RapidMiner is a complete data science platform focusing on delivering business value [166]. It brings together data preparation, machine learning and model operations to boost users' productivity of all skill levels within an organisation. The Konstanz Information Miner (KNIME) is a free and open-source platform for data analyses, reporting and integration [167]. Through its modular data pipelining 'Building Blocks of Analytics' concept, KNIME integrates multiple components for machine learning and data mining. The paid proprietary includes Angoss Knowledge STUDIO, Ayasdi, Amazon Machine Learning, IBM Watson Studio, Azure Machine Learning, IBM SPSS Modeler, Google Prediction API, Mathematica, KXEN Modeler, STATISTICA Data Miner, LIONsolver, Oracle Data Mining, MATLAB, Oracle AI Platform Cloud Service, Neural Designer, NeuroSolutions, SAS Enterprise Miner, Splunk, SequenceL, PolyAnalyst and RCASE.

#### **4. Machine learning and biochar: past and the future**

#### **4.1 Classification of machine learning algorithms**

For new users, selecting 'which algorithm to study' can be tough. Machine learning algorithms have their own set of advantages and disadvantages. Some excel with textual data, others excel at visuals and others at other data types. Many characteristics, such as resemblance, behaviour, data kinds and others, can be used to classify machine learning algorithms [168, 169].

Linear Regression, Logistic Regression, Decision Tree, SVM (Support Vector Machine) Algorithm, Naive Bayes Algorithm, KNN (K-Nearest Neighbours) Algorithm, K-Means and Random Forest Algorithm are some of the most used machine learning algorithms [170–172] as shown in **Figure 7**.

#### **4.2 Machine learning algorithms used in biochar**

Some selected works have been done using machine learning in biochar optimisation, which is dependent on the design of experiments for identifying pyrolysis parameters and optimising processes, which are all influenced by interconnected elements. The literature optimisation is separated into two categories: production and use. The optimisation procedure maximises the biochar's adsorption capacity and effectiveness for environmental and water remediation by antibiotics, extracting heavy metals and other contaminants from industrial effluent [174]. The three most significant process parameters in biochar manufacture are the heating temperature, heating time and heating rate [175]. The gaseous environment and particle size

**Figure 7.** *Classification of machine learning algorithms [173].*

employed in the biochar production variable such as the moisture contents, presence of inorganic/organic elements that catalyse certain reaction were included as feedstock factors for optimisation.

#### *4.2.1 Yield prediction via machine learning*

The algal biochar yield was predicted via extreme gradient algorithms. The XGB (eXtreme Gradient Boosting) machine-learning algorithm was used for prediction of algal biochar composition and yield in this study. In the XGB model, an intensive grid search strategy was designed to evaluate all of the available input parameter combination for forecasting biochar yield. Thirteen distinct pyrolytically significant input parameters combination were compared with the combination indicated by the model's techniques selection feature to predict biochar yield. The ash content, N/C, pyrolysis temperature, H/C and duration are essential parameters in determining the algal biochar output in this feature selection technique, where N, H and C are the nitrogen, hydrogen and carbon biomass content, respectively. Once the model was trained with the training data set, the highest R<sup>2</sup> of 0.84 was attained between model predictive and experimental biochar yield for the data set test. A Pearson correlation coefficients matrix showed the link between the biochar yield and input parameters. The Feature Temperature was the most significant element in plots. The interactive influence of other input parameter and temperature on algal charcoal output was represented using Shapley Additive exPlanations (SHAP) Dependence Plot. The plots' summary revealed the relevant features combined with SHAP and feature values.

The created XGB model adds to our understanding of the input parameter impact on algal biochar yield prediction.

Zhu et al.'s [176] machine learning was utilised in this study to construct prediction models for yield and carbon content of biochar (C-char) based on pyrolysis data of lignocellulosic biomass and investigate the inner information underlying the models. Based on biomass properties and pyrolysis circumstances, the results revealed that random forests could reliably forecast biochar output and C-char. Furthermore, for both yield (65%) and C-char, the proportional contribution of pyrolysis conditions was higher than that of biomass characteristics (53%). Structural information was more significant than element compositions for biomass characteristics for effectively estimating biochar yield, and the opposite was true for C-char. In the pyrolysis process, the partial dependence plot analysis revealed the impact of each important component on the target variable and the interactions between these elements. The study added the biomass pyrolysis process knowledge and improved biochar yield and C-char quality.

Sun et al. [177] studied the application of machine learning methods to predict metal immobilisation remediation by biochar amendment in soil. The work began by compiling and categorising data from published literature to develop a biochar soil remediation database, which now contains 930 data sets with 74 biochars and 43 soils. Then, based on biochar characteristics, soil physicochemical properties, incubation conditions (e.g. water holding capacity and remediation time) and the initial state of heavy metals, it modelled the remediation of five heavy metals and metalloids (lead, cadmium, arsenic, copper and zinc) by biochars using machine learning (ML) methods such as artificial neural network (ANN) and random forest (RF) to predict remediation efficiency. The ANN and RF models surpass the accuracy and predictive performance of the linear model (R2 > 0.84). Meanwhile, the anticipated outputs of the models investigated model tolerance for missing data and interpolation reliability. Both the ANN and the RF models performed admirably, with the RF model having a higher tolerance for missing data. Finally, the contribution of factors employed in the model was assessed using ML models' interpretability. And the findings revealed that the type of heavy metals, the pH value of biochar and the dosage and remediation period were the most influential elements of remediation. The relative importance of variables could point researchers on the proper path for better heavy metal cleanup in soil.

Cao et al. [178] employed SVM (support-vector machine) approach for estimation of the biochar output from cattle dung pyrolysis in their study. The parameters employed for modelling were moisture content, pyrolysis temperature, biochar yield, biochar mass, sample mass and heating rate, and they were based on a data set of 33 experimental data. The following metrics were used to assess the performance: Magnitudes of root mean square error (RMSE), average percent relative error (APRE), average absolute percent relative error (AAPRE) and coefficient of discrimination (R2 ). To compare the resilience and properties of SVM, an ANN model was created. Surprisingly, SVM outperformed ANN with an R<sup>2</sup> score of 0.9625, whilst ANN's R2 value was 0.8040.

Li et al. [179] compiled information from prior studies to create a predictive model for biochar qualities depending on feedstock and pyrolysis settings. Though significant biochar properties such as pH, yield, specific surface area, cation exchange capacity, volatile matter content, ash content and elemental compositions are affected by different factors, there is strong link between biochar properties, feedstock type and pyrolysis temperature.

#### *4.2.2 Distributing heavy metal via machine learning*

Heavy metal testing using traditional spectral approaches is time-consuming and impossible to detect for huge amounts of effluent. Based on remote sensing imagery, geographical data and spatial distribution, machine learning algorithm may be utilised to forecast effluents metal distribution. RF, SVM and ANN have been used for this.

RF and ANN machine learning algorithm were utilised in predicting the heavy metals concentration present in soil using visible and infrared spectroscopy data [180]. Also, Zhang et al. [181] used geographical distribution data, and the concentrations of Cd, As, Cu, Zn, Pb, Cr, Hg and Ni in the soil were predicted via SVM, RF and ANN algorithms. Hu et al. [182] utilised RF to find the regulating factors in heavy metal bioaccumulation in soil-crop systems. ANN is a simple method for determining the link between the heavy metal pollutants removal and process parameter [133, 183].

#### *4.2.3 Pyrolysis parameter*

In recent literature, ANN has been primarily utilised to optimise pyrolysis parameters, but techniques such as the Taguchi approach have also been applied. This application creates orthogonal matrices using a basic statistical tool to conceptualise an integrated experimental design to discover crucial factors in an optimised operation [175]. For effective optimisation, ANN is employed in conjunction with other technologies. In Lakshmi et al. [126], a unique approach is described that combines several types of ANN in conjunction with techniques such as particles swarm optimisation to almost always guarantee global optimum without local minimum trapping. Particles swarm optimisation is novel, efficient, rapid, robust and simple when tackling non-linear, multi-variable problems. Razzaghi et al. [183] employ genetic algorithms to optimise the generated ANN, resulting in process parameter values.

#### *4.2.4 Metal remediation and machine learning*

Machine learning could be useful in developing predictive models for heavy metals cleanup utilising modified biochar. ML models are useful in the adsorption process because of their ability to analyse intricate correlations between factors [184]. ML models are an effective modelling tool in the adsorption process because of their capacity to improve analysed relationships among numerous parameters [185]. The performance of adsorption is affected by operational parameters such as heating rate, temperature, dosage, adsorbent surface area, particle size, starting concentration, pH and contact time value. Taking all of this into account, constructing adsorption models is time-consuming and takes a lot of experimentation. To avoid this tedium, ML can be used to create robust models in evaluating the heavy metals adsorption process [186–189].

Wong et al. [184] examined the operational parameters effect such as dosage, contact time, operating temperature and biochar initial concentration on the process of adsorption using rambutan peel biochar to remove Cu(II) from water body. They used AI models such as Multi-Layer Regression, ANN and ANFIS to study the impact of the above-mentioned operational parameters (MLR). Adaptive neuro-fuzzy inference system (ANFIS) is a Neuro-Fuzzy intelligent modelling and control technique for ill-defined and unpredictable systems. The system's input/output data pairs under examination form the basis of ANFIS. The ANFIS model was the most accurate, with 90.24% score, followed by 88.27% ANN and 59.14% MLR. For Pb(II) adsorption on ethylenediaminetetraacetic acid (EDTA) treated biochar, Li et al. [190] constructed an AI model utilising the SVM algorithm.

Nath and Sahu [155] employed iron oxides infused mesoporous rice-husk nanobiochar in removing arsenic. Using ANN and RSM methodologies, they obtained a removal efficiency of 96%. Six AI models was developed by Afridi [173] with different architectures network in ANN for prediction of heavy metal adsorptions on modified biochar. The six models were effective, with R<sup>2</sup> values greater than 0.99 between predicted and expected variables. Chakraborty and Das [191] developed an ANN model to estimate Cr (VI) absorption efficiency on sawdust biochar nanocomposite. The ANN model assisted them in determining an appropriate adsorption mechanisms and the most excellent feasible Cr (VI) equations for absorption on biochar modified.

Zhao et al. [192] demonstrated a new method to establish sensitive parameter impacting the process of adsorption and develop strong predictive model using AI. For prediction of the efficiency of six metal ions adsorption, the authors used kernel extreme learning machine, with SVM and Kriging model subset. These models accurately identified sensitive parameters, such as T, pH water, ionic radius, total carbon ratio and pH solute, with R2 above 0.9, and could provide the necessary framework for developing predictive models for various scenarios.

Zhu et al. [193] investigated the application of machine learning methods to predict metal sorption onto biochars. The study used 353 data sets of adsorption studies from works of literature, the adsorption of six heavy metals (lead, cadmium, nickel, arsenic, copper and zinc) on 44 biochars was predicted using artificial neural networks (ANNs) and random forests (RF). The regression models were trained and refined to estimate adsorption capacity based on biochar properties, metal sources, environmental factors (temperature and pH) and the initial metal-to-biochar concentration ratio. The study discovered that RF model was more accurate than the ANN model.

Machine learning may be used to forecast and automate the remediation process and optimise process variables and feedstock conditions for optimal heavy metal removal efficiency. Machine learning may be utilised to create kinetic models and hybrid isotherm, which will accurately model for multicomponent systems and reduce error making the removal of heavy metal more cost-effective and efficient time.

#### **5. Conclusions**

The study was able to draw a relationship between biochar and machine learning. A review of biochar from history to application and challenges was discussed. Remediation of heavy metal is critical to avoid bioaccumulation, soil degradation and environmental contamination. Biochar is a practical and inexpensive method for removing heavy metal from waste effluent. Various approaches can improve the removal heavy metals effectively from pristine biochar. The paper also gave an overview of machine learning. Various algorithms of machine learning were discussed. After that, selected algorithms used for biochar were reviewed, and areas of opportunities were discussed. Artificial neural networks, support-vector models and random forests have been deployed in the machine learning of biochar. The ANN and RF

models surpass the accuracy and predictive performance of the linear model. It was seen that random forest models perform better than artificial neural network models for predicting and generalisation. Machine learning will lead to a greater understanding of biochar's effectiveness and applications in more sectors.

### **Acknowledgements**

The authors acknowledge the funding from URC of the University of Johannesburg and the National Research Foundation of South Africa.

### **Conflict of interest**

The authors declare that there is no conflict of interest.

### **Author details**

Kingsley Ukoba\* and Tien-Chien Jen University of Johannesburg, Johannesburg, South Africa

\*Address all correspondence to: ukobaking@yahoo.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### **References**

[1] Skilton M, Hovsepian F. The 4th Industrial Revolution. Springer; 2018

[2] Granatstein D et al. Use of Biochar from the Pyrolysis of Waste Organic Material as a Soil Amendment. Washington State University; 2009

[3] Kim Y et al. Modification of biochar properties using CO2. Chemical Engineering Journal. 2019;**372**:383-389

[4] Morrar R, Arman H, Mousa S. The fourth industrial revolution (Industry 4.0): A social innovation perspective. Technology Innovation Management Review. 2017;**7**(11):12-20

[5] Ramola S, Belwal T, Srivastava RK. Thermochemical conversion of biomass waste-based biochar for environment remediation. In: Handbook of Nanomaterials and Nanocomposites for Energy and Environmental Applications. 2020. pp. 1-16

[6] Parvez AM et al. Utilization of CO2 in thermochemical conversion of biomass for enhanced product properties: A review. Journal of CO2 Utilization. 2020;**40**:101217

[7] Imoisili PE et al. Production and characterization of hybrid briquette from biomass. British Journal of Applied Science & Technology. 2014;**4**(10):1534

[8] Kofi A-K. Design, Construction and Testing of a Biocharring Unit. Kwame Nkrumah University of Science And Technology Kumasi; 2014

[9] Yousaf B et al. Investigating the biochar effects on C-mineralization and sequestration of carbon in soil compared with conventional amendments using

the stable isotope (δ13C) approach. GCB Bioenergy. 2017;**9**(6):1085-1099

[10] Woolf D et al. Sustainable biochar to mitigate global climate change. Nature Communications. 2010;**1**(1):1-9

[11] Ding Y et al. Biochar to improve soil fertility. A review. Agronomy for sustainable development. 2016;**36**(2):1-18

[12] Gupta R et al. Rice straw biochar improves soil fertility, growth, and yield of rice–wheat system on a sandy loam soil. Experimental Agriculture. 2020;**56**(1):118-131

[13] Aup-Ngoen K, Noipitak M. Effect of carbon-rich biochar on mechanical properties of PLA-biochar composites. Sustainable Chemistry and Pharmacy. 2020;**15**:100204

[14] Man KY et al. Use of biochar as feed supplements for animal farming. Critical Reviews in Environmental Science and Technology. 2021;**51**(2):187-217

[15] Weber K, Quicker P. Properties of biochar. Fuel. 2018;**217**:240-261

[16] Palansooriya KN et al. Prediction of soil heavy metal immobilization by biochar using machine learning. Environmental Science & Technology. 2022

[17] Zhu X et al. Machine learning exploration of the direct and indirect roles of Fe impregnation on Cr (VI) removal by engineered biochar. Chemical Engineering Journal. 2022;**428**:131967

[18] Paula AJ et al. Machine learning and natural language processing enable a data-oriented experimental design

approach for producing biochar and hydrochar from biomass. Chemistry of Materials. 2022

[19] Singh A, Thakur N, Sharma A. A review of supervised machine learning algorithms. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom). IEEE; 2016

[20] Crisci C, Ghattas B, Perera G. A review of supervised machine learning algorithms and their applications to ecological data. Ecological Modelling. 2012;**240**:113-122

[21] Wang J, Wang S. Preparation, modification and environmental application of biochar: A review. Journal of Cleaner Production. 2019;**227**:1002-1022

[22] Ronsse F, Nachenius RW, Prins W. Carbonization of biomass. In: Recent Advances in Thermo-Chemical Conversion of Biomass. Elsevier; 2015. pp. 293-324

[23] Hayes M. Biochar and biofuels for a brighter future. Nature. 2006;**443**(7108):144-144

[24] Fulton W et al. Biochars as a potting amendment in greenhouse nurseries: How to eliminate unwanted ethylene 1 emissions 2. Agronomy for Sustainable Development. 2013;**33**(3)

[25] Dooley J et al. Moderate-Scale Biochar Production Across Forested Landscapes

[26] Bezerra J et al. The promises of the Amazonian soil: Shifts in discourses of Terra Preta and biochar. Journal of Environmental Policy & Planning. 2019;**21**(5):623-635

[27] Guo M, He Z, Uchimiya SM. Introduction to biochar as an agricultural and environmental amendment. Agricultural and Environmental Applications of Biochar: Advances and Barriers. 2016;**63**:1-14

[28] Boateng AA et al. Biochar production technology. In: Biochar for Environmental Management. Routledge; 2015. pp. 95-120

[29] Kan T, Strezov V, Evans TJ. Lignocellulosic biomass pyrolysis: A review of product properties and effects of pyrolysis parameters. Renewable and Sustainable Energy Reviews. 2016;**57**:1126-1140

[30] Tripathi M, Sahu JN, Ganesan P. Effect of process parameters on production of biochar from biomass waste through pyrolysis: A review. Renewable and Sustainable Energy Reviews. 2016;**55**:467-481

[31] Palanivelu K, Ramachandran A, Raghavan V. Biochar from biomass waste as a renewable carbon material for climate change mitigation in reducing greenhouse gas emissions—A review. Biomass Conversion and Biorefinery. 2021;**11**(5):2247-2267

[32] Suman S, Gautam S. Pyrolysis of coconut husk biomass: Analysis of its biochar properties. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2017;**39**(8):761-767

[33] Wang Z et al. Biochar produced from oak sawdust by lanthanum (La) involved pyrolysis for adsorption of ammonium (NH4+), nitrate (NO3−), and phosphate (PO43−). Chemosphere. 2015;**119**:646-653

[34] Cha JS et al. Production and utilization of biochar: A review. Journal of Industrial and Engineering Chemistry. 2016;**40**:1-15

*Biochar and Application of Machine Learning: A Review DOI: http://dx.doi.org/10.5772/intechopen.108024*

[35] Velusamy K et al. A review on nano-catalysts and biochar-based catalysts for biofuel production. Fuel. 2021;**306**:121632

[36] Martinez CLM et al. Evaluation of thermochemical routes for the valorization of solid coffee residues to produce biofuels: A Brazilian case. Renewable and Sustainable Energy Reviews. 2021;**137**:110585

[37] Bridgwater AV. Review of fast pyrolysis of biomass and product upgrading. Biomass and Bioenergy. 2012;**38**:68-94

[38] Tenenbaum DJ. Biochar: Carbon Mitigation from the Ground Up. National Institute of Environmental Health Sciences; 2009

[39] Lin Q et al. Assessing the potential of biochar and aged biochar to alleviate aluminum toxicity in an acid soil for achieving cabbage productivity. Ecotoxicology and Environmental Safety. 2018;**161**:290-295

[40] Jaiswal AK et al. Activating biochar by manipulating the bacterial and fungal microbiome through pre-conditioning. New Phytologist. 2018;**219**(1):363-377

[41] Tomczyk A, Sokołowska Z, Boguta P. Biochar physicochemical properties: Pyrolysis temperature and feedstock kind effects. Reviews in Environmental Science and Bio/Technology. 2020;**19**(1):191-215

[42] Janu R et al. Biochar surface functional groups as affected by biomass feedstock, biochar composition and pyrolysis temperature. Carbon Resources Conversion. 2021;**4**:36-46

[43] Zhang C et al. Evolution of the functional groups/structures of biochar and heteroatoms during the pyrolysis of seaweed. Algal Research. 2020;**48**:101900

[44] Chen Y et al. The structure evolution of biochar from biomass pyrolysis and its correlation with gas pollutant adsorption performance. Bioresource Technology. 2017;**246**:101-109

[45] Fellet G et al. Application of biochar on mine tailings: Effects and perspectives for land reclamation. Chemosphere. 2011;**83**(9):1262-1267

[46] Brewer CE et al. New approaches to measuring biochar density and porosity. Biomass and Bioenergy. 2014;**66**:176-185

[47] Atkinson CJ, Fitzgerald JD, Hipps NA. Potential mechanisms for achieving agricultural benefits from biochar application to temperate soils: A review. Plant and Soil. 2010;**337**(1):1-18

[48] Al-Wabel MI et al. Impact of biochar properties on soil conditions and agricultural sustainability: A review. Land Degradation & Development. 2018;**29**(7):2124-2161

[49] Monnie F. Effect of Biochar on Soil Physical Properties, Water Use Efficiency, and Growth of Maize in a Sandy Loam Soil. University of Ghana; 2016

[50] Liao W, Drake J, Thomas SC. Biochar granulation enhances plant performance on a green roof substrate. Science of the Total Environment. 2022;**813**:152638

[51] Gupta S, Kua HW. Carbonaceous micro-filler for cement: Effect of particle size and dosage of biochar on fresh and hardened properties of cement mortar. Science of the Total Environment. 2019;**662**:952-962

[52] Yang C, Liu J, Lu S. Pyrolysis temperature affects pore characteristics of rice straw and canola stalk biochars and biochar-amended soils. Geoderma. 2021;**397**:115097

[53] Esmaeelnejad L et al. Impacts of woody biochar particle size on porosity and hydraulic conductivity of biocharsoil mixtures: An incubation study. Communications in Soil Science and Plant Analysis. 2017;**48**(14):1710-1718

[54] Anderson N et al. A comparison of producer gas, biochar, and activated carbon from two distributed scale thermochemical conversion systems used to process forest biomass. Energies. 2013;**6**(1):164-183

[55] Shang L et al. Changes of chemical and mechanical behavior of torrefied wheat straw. Biomass and Bioenergy. 2012;**40**:63-70

[56] Singh M et al. Co-combustion properties of torrefied rice straw-subbituminous coal blend and its Hardgrove Grindability Index. In: Biomass Conversion and Biorefinery. 2021. pp. 1-15

[57] Zhao L et al. Heterogeneity of biochar properties as a function of feedstock sources and production temperatures. Journal of Hazardous Materials. 2013;**256**:1-9

[58] Rehrah D et al. Production and characterization of biochars from agricultural by-products for use in soil quality enhancement. Journal of Analytical and Applied Pyrolysis. 2014;**108**:301-309

[59] Rashid M et al. Prospects of biochar in alkaline soils to mitigate climate change. In: Environment, Climate, Plant and Vegetation Growth. Springer; 2020. pp. 133-149

[60] Lehmann J et al. Biochar in climate change mitigation. Nature Geoscience. 2021;**14**(12):883-892

[61] Oni BA, Oziegbe O, Olawole OO. Significance of biochar application to the environment and economy. Annals of Agricultural Sciences. 2019;**64**(2):222-236

[62] Verheijen F et al. Biochar application to soils. A critical scientific review of effects on soil properties, processes, and functions. EUR. 2010;**24099**:162

[63] Bolan N et al. Multifunctional applications of biochar beyond carbon storage. International Materials Reviews. 2021:1-51

[64] Brassard P et al. Biochar for soil amendment. In: Char and Carbon Materials Derived from Biomass. Elsevier; 2019. pp. 109-146

[65] Kamali M et al. Biochar for soil applications-sustainability aspects, challenges and future prospects. Chemical Engineering Journal. 2022;**428**:131189

[66] Syuhada AB et al. Biochar as soil amendment: Impact on chemical properties and corn nutrient uptake in a Podzol. Canadian Journal of Soil Science. 2016;**96**(4):400-412

[67] Mukherjee A, Lal R. The biochar dilemma. Soil Research. 2014;**52**(3):217-230

[68] Diatta AA et al. Effects of biochar on soil fertility and crop productivity in arid regions: A review. Arabian Journal of Geosciences. 2020;**13**(14):1-17

[69] Igalavithana AD et al. The effects of biochar amendment on soil fertility. In: Agricultural and Environmental Applications of Biochar: Advances and Barriers. sssaspecpub63 ed. 2016. pp. 123-144

[70] He M et al. A critical review on performance indicators for evaluating *Biochar and Application of Machine Learning: A Review DOI: http://dx.doi.org/10.5772/intechopen.108024*

soil biota and soil health of biocharamended soils. Journal of Hazardous Materials. 2021;**414**:125378

[71] Krishnakumar S et al. Impact of biochar on soil health. International Journal of Advanced Research. 2014;**2**(4):933-950

[72] Najar GR, Ganie MA, Tahir A. Biochar for sustainable soil health: A review of prospects and concerns. Pedosphere. 2015;**25**(5):639-653

[73] Das SK, Ghosh GK. Soil health management through low cost biochar technology. In: Biochar Applications in Agriculture and Environment Management. Springer; 2020. pp. 193-206

[74] Chan KY et al. Agronomic values of greenwaste biochar as a soil amendment. Soil Research. 2007;**45**(8):629-634

[75] Wu W et al. Chemical characterization of rice straw-derived biochar for soil amendment. Biomass and Bioenergy. 2012;**47**:268-276

[76] Chan K et al. Using poultry litter biochars as soil amendments. Soil Research. 2008;**46**(5):437-444

[77] Yadav A et al. Vacuum pyrolysed biochar for soil amendment. Resource-Efficient Technologies. 2016;**2**:S177-S185

[78] Nawkarkar P, Ganesan A, Kumar S. Carbon dioxide capture for biofuel production. In: Handbook of Biofuels. Elsevier; 2022. pp. 605-619

[79] Kral U. A new indicator for the assessment of anthropogenic substance flows to regional sinks. 2014

[80] Brückner R, Titgemeyer F. Carbon catabolite repression in bacteria: Choice of the carbon source and

autoregulatory limitation of sugar utilization. FEMS Microbiology Letters. 2002;**209**(2):141-148

[81] Buss W et al. Mineral-enriched biochar delivers enhanced nutrient recovery and carbon dioxide removal. Communications Earth & Environment. 2022;**3**(1):1-11

[82] Das SK, Ghosh GK, Avasthe R. Biochar application for environmental management and toxic pollutant remediation. Biomass Conversion and Biorefinery. 2020:1-12

[83] Geden O, Schenuit F. Unconventional mitigation: Carbon dioxide removal as a new approach in EU climate policy. 2020

[84] Möllersten K, Naqvi R, Yan J. Qualitative Assessment of Classes of Negative Emission Technologies (NETs). Västerås: Mälardalen University; 2020

[85] Johnson K et al. Carbon dioxide removal options: A literature review identifying carbon removal potentials and costs. 2017

[86] Williamsona P et al. 10. Biologicallybased negative emissions in the open ocean and coastal seas. 2021

[87] Yu O-Y, Raichle B, Sink S. Impact of biochar on the water holding capacity of loamy sand soil. International Journal of Energy and Environmental Engineering. 2013;**4**(1):1-9

[88] Ulyett J et al. Impact of biochar addition on water retention, nitrification and carbon dioxide evolution from two sandy loam soils. European Journal of Soil Science. 2014;**65**(1):96-104

[89] Abel S et al. Impact of biochar and hydrochar addition on water retention and water repellency of sandy soil. Geoderma. 2013;**202**:183-191

[90] Castellini M et al. Impact of biochar addition on the physical and hydraulic properties of a clay soil. Soil and Tillage Research. 2015;**154**:1-13

[91] Wang D et al. Impact of biochar on water retention of two agricultural soils—A multi-scale analysis. Geoderma. 2019;**340**:185-191

[92] Baronti S et al. Impact of biochar application on plant water relations in *Vitis vinifera* (L.). European Journal of Agronomy. 2014;**53**:38-44

[93] Novak JM et al. Impact of biochar amendment on fertility of a southeastern coastal plain soil. Soil Science. 2009;**174**(2):105-112

[94] Laird DA et al. Impact of biochar amendments on the quality of a typical Midwestern agricultural soil. Geoderma. 2010;**158**(3-4):443-449

[95] van de Ven DJ, Fouquet R. Historical energy price shocks and their changing effects on the economy. Energy Economics. 2017;**62**:204-216

[96] Xie T et al. Characteristics and applications of biochar for environmental remediation: A review. Critical Reviews in Environmental Science and Technology. 2015;**45**(9):939-969

[97] Mandal S et al. Progress and future prospects in biochar composites: Application and reflection in the soil environment. Critical Reviews in Environmental Science and Technology. 2021;**51**(3):219-271

[98] Shakoor MB et al. A review of biochar-based sorbents for separation of heavy metals from water. International Journal of Phytoremediation. 2020;**22**(2):111-126

[99] Shaheen SM et al. Wood-based biochar for the removal of potentially toxic elements in water and wastewater: A critical review. International Materials Reviews. 2019;**64**(4):216-247

[100] Inyang MI et al. A review of biochar as a low-cost adsorbent for aqueous heavy metal removal. Critical Reviews in Environmental Science and Technology. 2016;**46**(4):406-433

[101] Ltd ARP. Biochar Market Expected to Grow at 8.9% of High CAGR by Forecast 2028 With SWOT Analysis, Future Trends, Challenges and Growth Opportunities. USA: GlobeNewswire; 2022

[102] Bowling M et al. Machine learning and games. Machine Learning. 2006;**63**(3):211-215

[103] Samuel AL. Machine learning. The Technology Review. 1959;**62**(1):42-45

[104] El Naqa I, Murphy MJ. What is machine learning? In: Machine Learning in Radiation Oncology. Springer; 2015. pp. 3-11

[105] Gogas P, Papadimitriou T. Machine learning in economics and finance. Computational Economics. 2021;**57**(1):1-4

[106] Nilsson N. Learning Machines. Vol. 19652. New York: McGraw-Hill; 1965

[107] Clarke M. Pattern Classification and Scene Analysis. Wiley Online Library; 1974

[108] Abaimov S, Martellini M. Understanding machine learning. In: Machine Learning for Cyber Agents. Springer; 2022. pp. 15-89

[109] Bozinovski S. Reminder of the first paper on transfer learning in neural networks, 1976. Informatica. 2020;**44**(3)

*Biochar and Application of Machine Learning: A Review DOI: http://dx.doi.org/10.5772/intechopen.108024*

[110] Mitchell TM. Artificial neural networks. Machine learning. 1997;**45**:81-127

[111] Mitchell TM. Machine Learning. New York: McGraw-Hill; 1997

[112] Harnad S. The annotation game: On Turing (1950) on computing, machinery, and intelligence. In: The Turing Test Sourcebook: Philosophical and Methodological Issues in the Quest for the Thinking Computer. Kluwer; 2006

[113] Patel J et al. Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications. 2015;**42**(1):259-268

[114] Soni S. Applications of ANNs in stock market prediction: A survey. International Journal of Computer Science & Engineering Technology. 2011;**2**(3):71-83

[115] Mohri M, Rostamizadeh A, Talwalkar A. Foundations of Machine Learning. MIT Press; 2018

[116] Ethem AM. Introduction to Machine Learning/Ethem Alpaydın. 2010. London: The MIT Press

[117] Angluin D. Computational learning theory: Survey and selected bibliography. In: Proceedings of the twenty-fourth annual ACM symposium on Theory of computing. 1992

[118] Rashidi HH et al. Artificial intelligence and machine learning in pathology: The present landscape of supervised methods. Academic Pathology. 2019;**6**:2374289519873088

[119] Moujahid A et al. Machine learning techniques in ADAS: A review. In: 2018

International Conference on Advances in Computing and Communication Engineering (ICACCE). IEEE; 2018

[120] Liu D et al. Optimizing wireless systems using unsupervised and reinforced-unsupervised deep learning. IEEE Network. 2020;**34**(4):270-277

[121] Jiang T, Gradus JL, Rosellini AJ. Supervised machine learning: A brief primer. Behavior Therapy. 2020;**51**(5):675-687

[122] Burkart N, Huber MF. A survey on the explainability of supervised machine learning. Journal of Artificial Intelligence Research. 2021;**70**:245-317

[123] Jaeger S, Fulle S, Turk S. Mol2vec: Unsupervised machine learning approach with chemical intuition. Journal of Chemical Information and Modeling. 2018;**58**(1):27-35

[124] Sutton RS. Introduction: The challenge of reinforcement learning. In: Reinforcement Learning. Springer; 1992. pp. 1-3

[125] Oyetunde T et al. Leveraging knowledge engineering and machine learning for microbial biomanufacturing. Biotechnology Advances. 2018;**36**(4):1308-1315

[126] Lakshmi D et al. Artificial intelligence (AI) applications in adsorption of heavy metals using modified biochar. Science of the Total Environment. 2021;**801**:149623

[127] Zhou X et al. Hyperspectral technique combined with deep learning algorithm for detection of compound heavy metals in lettuce. Food Chemistry. 2020;**321**:126503

[128] Verma JK, Paul S, Johri P. Computational Intelligence and Its Applications in Healthcare. Academic Press; 2020

[129] Meza JKS et al. Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks. Heliyon. 2019;**5**(11):e02810

[130] Golbayani P, Florescu I, Chatterjee R. A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees. The North American Journal of Economics and Finance. 2020;**54**:101251

[131] Ghavamzadeh M et al. Bayesian reinforcement learning: A survey. Foundations and Trends in Machine Learning. 2015;**8**(5-6):359-483

[132] Mitra T et al. Removal of Pb (II) ions from aqueous solution using water hyacinth root by fixed-bed column and ANN modeling. Journal of Hazardous Materials. 2014;**273**:94-103

[133] Zafar M et al. Ethanol mediated As (III) adsorption onto Zn-loaded pinecone biochar: Experimental investigation, modeling, and optimization using hybrid artificial neural networkgenetic algorithm approach. Journal of Environmental Sciences. 2017;**54**:114-125

[134] Genuino DAD et al. Application of artificial neural network in the modeling and optimization of humic acid extraction from municipal solid waste biochar. Journal of Environmental Chemical Engineering. 2017;**5**(4):4101-4107

[135] Williams B et al. Data-driven model development for cardiomyocyte production experimental failure prediction. In: Computer Aided

Chemical Engineering. Elsevier; 2020. pp. 1639-1644

[136] Misra S, Li H, He J. Noninvasive fracture characterization based on the classification of sonic wave travel times. In: Machine Learning for Subsurface Characterization. 2020. pp. 243-287

[137] Pradhan A. Support vector machine-a survey. International Journal of Emerging Technology and Advanced Engineering. 2012;**2**(8):82-85

[138] Yao Y et al. K-SVM: An effective SVM algorithm based on K-means clustering. Journal of Computers. 2013;**8**(10):2632-2639

[139] Tharwat A, Hassanien AE, Elnaghi BE. A BA-based algorithm for parameter optimization of support vector machine. Pattern Recognition Letters. 2017;**93**:13-22

[140] Natekin A, Knoll A. Gradient boosting machines, a tutorial. Frontiers in Neurorobotics. 2013;**7**:21

[141] Touzani S, Granderson J, Fernandes S. Gradient boosting machine for modeling the energy consumption of commercial buildings. Energy and Buildings. 2018;**158**:1533-1543

[142] Gong M et al. Gradient boosting machine for predicting return temperature of district heating system: A case study for residential buildings in Tianjin. Journal of Building Engineering. 2020;**27**:100950

[143] Fujiyoshi H, Hirakawa T, Yamashita T. Deep learning-based image recognition for autonomous driving. IATSS Research. 2019;**43**(4):244-252

[144] Deng L, Li X. Machine learning paradigms for speech recognition: An overview. IEEE Transactions on Audio, *Biochar and Application of Machine Learning: A Review DOI: http://dx.doi.org/10.5772/intechopen.108024*

Speech, and Language Processing. 2013;**21**(5):1060-1089

[145] Chen L et al. Machine learningbased product recommendation using apache spark. In: 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/ SCALCOM/UIC/ATC/CBDCom/IOP/ SCI). IEEE; 2017

[146] Soni A et al. Design of a machine learning-based self-driving car. In: Machine Learning for Robotics Applications. Springer; 2021. pp. 139-151

[147] Kononenko I. Machine learning for medical diagnosis: History, state of the art and perspective. Artificial Intelligence in Medicine. 2001;**23**(1):89-109

[148] Richens JG, Lee CM, Johri S. Improving the accuracy of medical diagnosis with causal machine learning. Nature Communications. 2020;**11**(1):1-9

[149] Tenni J et al. Machine learning of language translation rules. In: IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 99CH37028). IEEE; 1999

[150] De Prado ML. The 10 reasons most machine learning funds fail. The Journal of Portfolio Management. 2018;**44**(6):120-133

[151] Hong J-W, Wang Y, Lanz P. Why is artificial intelligence blamed more? Analysis of faulting artificial intelligence for self-driving car accidents in experimental settings. International Journal of Human–Computer Interaction. 2020;**36**(18):1768-1774

[152] Stilgoe J. Who killed Elaine Herzberg? In: Who's Driving Innovation? Springer; 2020. pp. 1-6

[153] Becker A. Artificial intelligence in medicine: What is it doing for us today? Health Policy and Technology. 2019;**8**(2):198-205

[154] Yampolskiy RV. Artificial intelligence safety engineering: Why machine ethics is a wrong approach. In: Philosophy and Theory of Artificial Intelligence. Springer; 2013. pp. 389-396

[155] Nath R, Sahu V. The problem of machine ethics in artificial intelligence. AI & Society. 2020;**35**(1):103-111

[156] Sandel MJ. The Case against Perfection: Ethics in the Age of Genetic Engineering. Harvard University Press; 2007

[157] Lucivero F. Ethical Assessments of Emerging Technologies. Cham: Springer; 2016

[158] Hinton G et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine. 2012;**29**(6):82-97

[159] Kobielus J. Gpus continue to dominate the ai accelerator market for now. 2019

[160] Ray T. AI Is Changing the Entire Nature of Compute. ZDNet; 2019. p. 30

[161] Chua L, Sirakoulis GC, Adamatzky A. Handbook of Memristor Networks. Springer Nature; 2019

[162] Zhu X, Wang Q, Lu WD. Memristor networks for real-time neural activity analysis. Nature Communications. 2020;**11**(1):1-9

[163] David R et al. TensorFlow lite micro: Embedded machine learning for tinyml systems. Proceedings of Machine Learning and Systems. 2021;**3**:800-811

[164] Gualtieri M. The Forrester Wave™: Predictive Analytics and Machine Learning Solutions, Q1. Forrester Research; 2017. p. 2017

[165] Dwivedi S, Kasliwal P, Soni S. Comprehensive study of data analytics tools (RapidMiner, Weka, R tool, Knime). In: 2016 Symposium on Colossal Data Analysis and Networking (CDAN). IEEE; 2016

[166] Grimes S. Text Analytics 2014: User Perspectives on. 2014

[167] Mazanetz MP et al. Drug discovery applications for KNIME: An open source data mining platform. Current Topics in Medicinal Chemistry. 2012;**12**(18):1965-1979

[168] Khan A et al. A review of machine learning algorithms for text-documents classification. Journal of Advances in Information Technology. 2010;**1**(1):4-20

[169] Mahesh B. Machine learning algorithms—A review. International Journal of Science and Research (IJSR). 2020;**9**:381-386

[170] Ibrahim I, Abdulazeez A. The role of machine learning algorithms for diagnosing diseases. Journal of Applied Science and Technology Trends. 2021;**2**(01):10-19

[171] Goeschel K. Reducing false positives in intrusion detection systems using data-mining techniques utilizing support vector machines, decision trees, and naive Bayes for off-line analysis. In: SoutheastCon 2016. IEEE; 2016

[172] Abdulrahman SA et al. Comparative study for 8 computational intelligence algorithms for human identification. Computer Science Review. 2020;**36**:100237

[173] Afridi R. List of Machine Learning Algorithms. 2020

[174] Hong S-H et al. Synthesis of Fe-impregnated biochar from food waste for selenium (VI) removal from aqueous solution through adsorption: Process optimization and assessment. Chemosphere. 2020;**252**:126475

[175] Hodgson E et al. Optimisation of slow-pyrolysis process conditions to maximise char yield and heavy metal adsorption of biochar produced from different feedstocks. Bioresource Technology. 2016;**214**:574-581

[176] Zhu X, Li Y, Wang X. Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions. Bioresource Technology. 2019;**288**:121527

[177] Sun Y et al. The application of machine learning methods for prediction of metal immobilization remediation by biochar amendment in soil. Science of the Total Environment. 2022:154668

[178] Cao H, Xin Y, Yuan Q. Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach. Bioresource Technology. 2016;**202**:158-164

[179] Li S et al. Predicting biochar properties and functions based on feedstock and pyrolysis temperature: A review and data syntheses. Journal of Cleaner Production. 2019;**215**:890-902

[180] Pyo J et al. Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of

*Biochar and Application of Machine Learning: A Review DOI: http://dx.doi.org/10.5772/intechopen.108024*

soil. Science of the Total Environment. 2020;**741**:140162

[181] Zhang H et al. Promotional effect of NH3 on mercury removal over biochar thorough chlorine functional group transformation. Journal of Cleaner Production. 2020;**257**:120598

[182] Hu B et al. Modelling bioaccumulation of heavy metals in soil-crop ecosystems and identifying its controlling factors using machine learning. Environmental Pollution. 2020;**262**:114308

[183] Razzaghi M et al. Phenol removal by HRP/GOx/ZSM-5 from aqueous solution: Artificial neural network simulation and genetic algorithms optimization. Journal of the Taiwan Institute of Chemical Engineers. 2018;**89**:1-14

[184] Wong YJ et al. Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (*Nephelium lappaceum*) peel. Environmental Monitoring and Assessment. 2020;**192**(7):1-20

[185] El Hanandeh A, Mahdi Z, Imtiaz M. Modelling of the adsorption of Pb, Cu and Ni ions from single and multi-component aqueous solutions by date seed derived biochar: Comparison of six machine learning approaches. Environmental Research. 2021;**192**:110338

[186] Talebkeikhah F et al. Investigation of effective processes parameters on lead (II) adsorption from wastewater by biochar in mild air oxidation pyrolysis process. International Journal of Environmental Analytical Chemistry. 2020:1-21

[187] Hafsa N et al. A generalized method for modeling the adsorption of heavy metals with machine learning algorithms. Water. 2020;**12**(12):3490

[188] Yaseen ZM. An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions. Chemosphere. 2021;**277**:130126

[189] Wanyonyi FS et al. Role of pore chemistry and topology in the heavy metal sorption by zeolites: From molecular simulation to machine learning. Computational Materials Science. 2021;**195**:110519

[190] Li M et al. EDTA functionalized magnetic biochar for Pb (II) removal: Adsorption performance, mechanism and SVM model prediction. Separation and Purification Technology. 2019;**227**:115696

[191] Chakraborty V, Das P. Synthesis of nano-silica-coated biochar from thermal conversion of sawdust and its application for Cr removal: Kinetic modelling using linear and nonlinear method and modelling using artificial neural network analysis. Biomass Conversion and Biorefinery. 2020:1-11

[192] Zhao Y et al. Application of kernel extreme learning machine and kriging model in prediction of heavy metals removal by biochar. Bioresource Technology. 2021;**329**:124876

[193] Zhu X, Wang X, Ok YS. The application of machine learning methods for prediction of metal sorption onto biochars. Journal of Hazardous Materials. 2019;**378**:120727

### **Chapter 15**

## Applications and Data Analysis Using Bayesian and Conventional Statistics in Biochar Adsorption Studies for Environmental Protection

*Obey Gotore,Tirivashe Phillip Masere, Osamu Nakagoe, Vadzanayi Mushayi, Ramaraj Rameshprabu, Yuwalee Unpaprom and Tomoaki Itayama*

#### **Abstract**

The use of low-cost agricultural waste-derived biochar in solving water and environmental challenges induced by climate change was investigated and sound conclusions were presented. Water reuse strategies can diminish the impact of climate change in rural and remote areas of developing countries. The novel biochar materials from three agro-waste biomass (Matamba fruit shell, Mushuma, and Mupane tree barks) were investigated and characterized to attest to their capacity to remove iodine from the aqueous solution. Their surface morphologies were assessed using Field Emission Scanning Electron Microscopy with Energy Dispersive X-Ray Spectroscopy (FESEM-EDX) which exhibited their structural phenomena to purge environmental pollutants. The Fourier-transform infrared spectroscopy (FTIR) was conducted to show surface functional groups of the biochar materials and Matamba fruit shell exhibited hydroxyl (-OH), carbonyl groups (C=O), C=C stretches of aromatic rings, and the carboxylate (C–O–O–) groups on its surface with corresponding data from the Isotherm and Kinetic models, statistically analyzed by the conventional and Bayesian methods. These surface mechanisms are said to be induced by weak van der Waals forces and and -stacking interaction on the biochar surface. These adsorbents promised to be potential materials for environmental-ecosystem-protection and water re-use approach.

**Keywords:** adsorption, Bayesian statistics, Matamba fruit shell, Mushuma bark biochar, Mupane bark biochar

#### **1. Introduction**

Global warming and climate change is triggering some drastic global environmental complications and developing countries are facing cumulative water insufficiency and such problems are subsequently increasing [1]. Some developing countries like Africa, South-East Asia, and South America are facing water deficits triggered by climate changes [2]. These countries are very vulnerable because of extremes of climatic change, which are increasing, their magnitude and frequency are making the availability of water a challenge to their societal livelihood's sustainability [3]. Portable safe water availability is becoming scarce due to high toxicity contaminants in water sources with a variety of constituents such as dyes [4] just to mention a few. Innovations, adaptations, and developments are being put in place to alleviate such burdens, and paramount measures are being employed to make sure that water is available and accessible to all in developing countries.

This study aims to remove micropollutants and recommend better wastewater reuse technology for unserved rural communities in an off-the-grid system to achieve socio-economic development using physiochemical properties of Mushuma, Mupani barks, and Matamba fruit shells, by analyzing their characteristics to evaluate the kinetic mechanism of adsorption from different models and statistical methods for the determination of equilibrium analysis. Thus, waste or residual biomass utilization such as biochar production has been given substantial attention because of its potential for carbon sequestration, waste management, and environmental remediation of pollutants [5]. A lot of technologies have been employed to mitigate challenges of water pollution globally, from a variety of sources and types of industries, for example, photocatalytic degradation [6], photooxidative degradation [7], Fenton reagent [8], and adsorption, wherein it is highly efficient in the removal of dyes and pigments from the liquid phase [9].

During the adsorption process, activated carbon is normally used due to its large specific surface area, well-developed pore structure, increased adsorption efficiency as well as good chemical stability [3]. [10] reiterated that adsorption takes place in the mesopores which act as conduits for adsorbate particles, and capillary condensation takes place to adsorb these macromolecules. The adsorption process has proved to be one of the best wastewater treatment technologies in the world and activated carbon acts as the universal adsorbent for the removal of different types of water pollutants. Most materials that come from carbon have great surface areas, which are stable with extensive functional groups, interconnected pore structures, and shapes [11]. To add to that, the Matamba fruit shell adsorption capacity, commonly found in Zimbabwe, can be a potential solution for water reuse techniques for the local people, and its kinetic adsorption was recently tested [11]. This can be attributed to adsorption as the most lucrative treatment technique [12], Langmuir and Freundlich's isotherms are common models which are extensively used since they are simple to use because of their empirical mathematical expressions [13].

The plant bark adsorbents for pollution reduction have widely been utilized in numerous studies which have been conducted recently [11]. Plant species analyzed included three eucalyptus, African border, flamboyant pods, and sycamore [14] among others. Biochar that is derived from waste biomass has also been considered one of the efficient adsorbents for wastewater pollutant removal because of its costeffective merits, easy obtainability, and beneficial physicochemical properties [3]. The activated carbon materials, as adsorbents, has merits but are not limited to the adequate surface area, porosity phenomena, and thermal stability [15].

Different researchers are studying the application of biochar for wastewater treatment [5], and various materials are being pyrolyzed under different conditions and they can affect the physicochemical properties of the product [16]. The use of chemicals to modify the biochar by acids, bases, or polymers seems to give better

*Applications and Data Analysis Using Bayesian and Conventional Statistics in Biochar… DOI: http://dx.doi.org/10.5772/intechopen.105868*

adsorption effectiveness because of enlarged surface area, modified chemical functionality, and availability of high-affinity adsorption sites [16]. Matamba, Mushuma, and Mupane tree barks as a novel and recent research for the removal of micropollutants as a wastewater re-use strategy or technology for unserved rural communities in an off-the-grid system to achieve socio-economic development.

#### **2. Production of biochar**

#### **2.1 Biomass materials and pyrolysis conditions**

Biochar is a material rich in carbon, produced from cracking several biomasses, such as wood, sludge, plants, food waste, and animal waste after carbonization [17, 18]. Such waste biomass materials exhibited to be effective for the removal of harmful substances from the aqueous solutions considering their less production cost and economic benefits, wide availability of raw material, and conducive surface properties. Furthermore, as noted by [5, 11, 19, 20], the use of biomass or residual waste has now been prioritized due to several advantages including environmental remediation, waste management, carbon sequestration, and ameliorating the greenhouse gas effect.

Pyrolysis is one of the most used technology in the production of biochar. It involves the carbonization of organic materials in limited or no oxygen conditions [21]. It is a thermochemical decomposition process taking place at temperatures above 300°C. In addition, the pyrolysis process may also produce volatile liquids and gases (e.g., carbon monoxide, carbon dioxide, hydrogen, methane, and biogas [22]. Pyrolysis may be categorized into four groups based on temperature conditions, reaction time, and heating rate. These are: slow, fast, flash, and intermediate, and of these slow and fast are the most common types [22]. In fast pyrolysis, the temperature and heating rates are higher than in slow pyrolysis. As such the process can be done in seconds and the resulting product consists mainly of bio-oils [23].

Conversely, in slow pyrolysis, the process can go on for hours and the heating rate and temperature are lower; a temperature under 450°C is commonly used and the resulting product is mainly biochar [22]. Biochar may be chemically modified using acids, bases, or polymers to have better adsorption efficiency due to the increased surface area, modified chemical functionality, and the presence of high-affinity adsorption sites [16]. The adsorption mechanism of the biochar after pyrolysis is shown on **Figure 1** where both positive and negative charges do exist on the surface due to thermal decomposition. This property enhancement process makes biochar a cost-effective choice hazardous material removal from the environment.

#### **2.2 Availability of Matamba fruit shell**

Matamba (monkey orange - Strychnos spp.) are widely distributed in Southern Africa and particularly in Zimbabwe, where they are generally found throughout the country, but more so in the Midlands Province [11, 24]. These fruits proliferate in semi-arid areas of Zimbabwe, with limited rainfall water, and produce the fruit in abundance [25, 26]. Depending on the season, excess production of the fruit varies and sometimes leads to its underutilization, and this can be seen in the highveld around Zimbabwe where fruit remains and disturbs the environment [24]. The Strychnos spp. fruit is extensively found in Zimbabwe, it is underutilized, and little or no consideration has been raised for potential commercialization due to limited

#### **Figure 1.**

*The adsorption mechanism of adsorbates onto the biochar and surface characteristics after pyrolysis.*

knowledge and dissemination of information about its propagation, agronomic practices, and product processing techniques for business [24].

The Matamba fruits begin to develop and grow during the autumn season and ripen in winter up to the spring season [11, 24]. The fruit is spherical with a hard thick shell, and the seeds are around 2–3 cm in diameter [11]. It is these seeds that are edible by humans and animals. To access these seeds the hard shells must be broken first, usually by hitting the shells on hard stone surfaces. After consumption of the seeds, the empty hard shells are often thrown away or littered around the veld or homesteads [24].

#### **2.3 Agro-waste biomass**

Agriculture and its related sectors like forestry generate massive volumes of biomass residues generated in the forestry in most developing countries. However, these residues should not be treated as waste given that a greater proportion of 'waste' is usable. As already discussed, pyrolysis is an important and more beneficial alternative to the usual farmer practice of burning, burying, or storing agricultural biomass residues [21, 27]. There is a large range of waste materials that could be suitable for pyrolysis and biochar production. However, for this study, agro-waste in the form of Mushuma, Mupane barks, and Matamba fruit shells were considered.

Mushuma tree, an African native species, is dominant in the Midlands province of Zimbabwe. The Shuma fruits (Jackle-berry, Diospyros mespiliformisare) [28] are syrup-like juice and smooth with a soft-transparent-jelly inside. The tree has a medium to huge tree stem with the outer bark peeling off naturally as the tree grows as well as the season changes. The Mupane tree (Colophospermum mopane) [28] is a legume family vegetation abundantly found in the Midlands of Zimbabwe in hot, dry low-lying areas with an altitude ranging from 200 to 1150 meters above sea level.

The Mupane tree is also prevalent in South Africa, particularly in the Northwestern part of that country [29]. Tree barks of Mushuma and Mupane are usually used to start fires because of their common availability in the province as well as their affinity to fires. It is very quick to start fires and the tree wood itself takes a long time without *Applications and Data Analysis Using Bayesian and Conventional Statistics in Biochar… DOI: http://dx.doi.org/10.5772/intechopen.105868*

extinguishing. Generally, these trees' bark is either left in the forest after the tree ages or the outer barks peeled off or used as fuel by the local communities. However due to high rural to urban movements, the availability of tree barks is increasing, and the rest is getting decomposed in the bush with no value to the community.

#### **3. Characterization of biomass materials**

#### **3.1 FESEM-EDX**

In efforts to understand the thermal transformation and the structural setup of Mupane, Mushuma tree bark as well as the Matamba fruit shell biochar, it was necessary to characterize their surfaces with field emission scanning electron microscopy (FESEM) and energy-dispersive X-ray spectroscopy (EDX) after pyrolysis. These materials were characterized by using FESEM (JEOL JSM-7500FAM Tokyo, Japan) for surface morphology and image generation and EDX for element composition of biochar with a low vacuum. The pretreatment of the samples was conducted, where the biochar samples were dried at 105°C for 4 hours, stuck on the copper plate using a black doublesided tape, vacuumed for 12 hours, and analyzed for surface transformation.

The outcome of the EDX conducted revealed the purity of the elemental composition of Mushuma, Mupane barks, and the Matamba fruit shell biochar. Principally, the Matamba fruit shell biochar exhibited to be made up of 72.68 wt% C and some significant elements such as 10.35 wt%, 14.14 wt%, 0.97 wt%, 0.46 wt%, 0.37 wt%, and 0.31 wt% of O, N, K, Mg, Ca, P respectively with some trace compounds of Si and S as shown in **Table 1**.

It was revealed that adequate content of the C element remained after pyrolysis greatly influenced the adsorption capacity (44.071 mmol/g) of the biochar as ascribed by the Elovich kinetic model. Furthermore, the available O composition also offers enough polarization capability for high adsorption of the iodine used (43.65 mmol/g) as observed in the experimental data.

#### **3.2 FTIR measurement**

The Fourier transform infrared spectroscopy (FTIR) analysis was carried out using a wave number scanning range between 400 and 4000 cm<sup>1</sup> . Before that, the content of the moisture and ash that can be available in these materials was measured following the ASTM D1762–84 guide. The elemental compositional analysis of C, H, and N was executed accordingly. Acetanilide was used as a standard. Approximately 2 mg of biochar was used for each measurement, and each measurement was carried out in triplicate. The oxygen content (O) was then determined by the difference between the original dried sample and the sum of C, H, N, and ash content.

#### **3.3 Surface area estimation using iodine solution**

The results from the EDX and FESEM elemental presentation show a high content of Caborn with rigid skeleton structures of Matamba fruit shell, Mushuma, and Mupane bark which would be ascribed to the residual lignin from incomplete pyrolysis of the materials. Moreover, the weak van der Waals forces played a role in the removal of Iodine due to these high C, C/N, and O/C ratios which are inferred in **Table 2** and augmented biochar surface meso pore filling.



*The elemental composition of Mushuma, Matamba, and Mupane biochar.*


**2.** *both the Bayesian and conventional statistical analysis results for the biochar investigated using isotherm and kinetic models.*

*Shows* 

#### *Applications and Data Analysis Using Bayesian and Conventional Statistics in Biochar… DOI: http://dx.doi.org/10.5772/intechopen.105868*

The remaining alkaline elements such as Ca, Mg, and K with inorganic basic minerals present might be ascribed to the main component of ash established from the pyrolysis process of the biochar [29, 30]. In summary, the Iodine adsorption mechanisms onto the investigated biochar materials made it a probable choice for environmental contamination option, water reuse possibility, and global warming reduction due to high C, C/N, and enough polarization propensity.

Regarding the biochar produced from Mushuma and Mupane barks, it was from the FESEM images above that surface texture can be influenced by biomass type even under identical pyrolysis conditions. Biochar produced from Mushuma bark has large surface pores (10–15 m in diameter), uniformly distributed and separated by a thick carbon wall (2–3 m) than Mupane bark. Biochar from Mupane bark had smaller and heterogeneously distributed pores (3–5 m in diameter).

Similarly, the kinetic results from Iodine adsorption indicated that biochar from Mupane had higher qt values than biochar from Mushuma bark. As found by [29], larger pores tend to correlate to the limited surface area than small pores, thus there is greater adsorption on smaller pores than on larger pores. Further, the small pores are associated with high porosity and void volume.

#### **4. Equilibrium mechanisms of adsorbents and data analysis**

#### **4.1 Adsorption kinetics of Matamba fruit shell and the tree bark adsorbents**

In principle, [31] elucidated that adsorption is known as the mass transfer method that entails some time for the adsorbate to diffuse from the bulk solution of the aqueous phase, through the solid–liquid film into the material's pore spaces and onto the available active sites. Therefore, based on the results obtained from the experiments kinetic models like pseudo-first-order (PFO), pseudo-second-order (PSO), intraparticle diffusion (IPD), and Elovich models are shown in **Table 3** and plotted as shown in **Figure 2a**.

MPNBC advocated more adsorption for Iodine than MSHBC as exhibited in **Figure 3c** and **d** correspondingly, however, Matamba fruit shell outperformed both tree barks. Subsequently, the Iodine kinetic adsorption mechanism on these materials could be divided into three stages: rapid adsorption stage, slow adsorption stage, and adsorption equilibrium stage as elucidated by [32] as well.

The first 12 hours were observed to be a rapid Iodine adsorption stage on both biochars. The graph for MPNBC seems to be steeper than MSHBC. The adsorption capacity of the prepared Mupane and Mushuma barks were estimated to be 40.38 and 39.78 mmol g <sup>1</sup> respectively, from the experimental data. From conventional statistical analysis of the Pseudo-second order model, Mushuma and Mupani biochar exhibited adsorption capacity of 40.01 and 40.29 mmol g <sup>1</sup> which were slightly lower than the Bayesian outcome of 40.712 and 41.639 mmol g <sup>1</sup> as shown in **Table 2**.

This reveals the strength of Bayesian analysis against classical statistics as different quantile ranges revealed different estimations and the 50% (median) was so close to the actual mean for each parameter. The adsorption rate constants of the two biochar also exhibited the above phenomenon where the conventional method indicated a homogeneous reaction rate (0.014 min <sup>1</sup> ), so as the Bayesian statistics as shown in **Table 2**. The figures also elucidated that the linear relationship is presented not as a continuous straight line but in two stages of least and enormous adsorption before and after 4 hr. of adsorption respectively.


*Applications and Data Analysis Using Bayesian and Conventional Statistics in Biochar… DOI: http://dx.doi.org/10.5772/intechopen.105868*

*The AIC scores were used for non-linear model selection instead of the coefficient of determination (R2 ).*

#### **Table 3.**

*The kinetic adsorption equations and estimated parameters from biochar materials after the experiment.*

The higher adsorption could be attributed to smaller biochar particle sizes (0.25–1.00 mm) used in this experiment which started to diffuse into the pores later since IPD is a slow process. The Elovich model revealed that the initial adsorption was 110.701 and 117.88 mmol g �<sup>1</sup> min �<sup>1</sup> , 112.847 and 120.214 mmol g �<sup>1</sup> min �<sup>1</sup> for Mupane and Mushuma biochar from Conventional and Bayesian methods respectively.

Different adsorption mechanisms could have been encountered during the 48 hour contact time, but the adsorption rate gradually decreased until the adsorption reaches the equilibrium state as [28] elaborated. For Matamba biochar, Elovich (Eq. (3)) and IPD (Eq. (4)) better describe the kinetic adsorption of biochar through iodine adsorption than PFO (Eq. (1)) and PSO (Eq. (2)) models. Generally, the iodine adsorption rate decreases exponentially as the amount of iodine adsorbed increases on the heterogeneous surfaces of the Mushuma, Mushuma, and Matamba fruit shell biochar. Several adsorption experiments have been reported to follow the Elovich kinetic model [33, 34].

However, the adsorption kinetic results from conventional statistics on the tree bark revealed that the PSO kinetic model better described the adsorption behaviors of the biochar for Iodine adsorption [35, 36]. The model selection AICc scores for MPNBC and MSHBC were 38.03 and 37.76 respectively, away below other models used. AICc is a strong tool for model selection than using the correlation coefficient on non-linear model functions. This can be theoretically supported by the equilibrium adsorption capacity values from both statistical methods were also close to the experimental equilibrium adsorption capacity, signifying that the pseudo-second-order kinetic model could better describe the Iodine adsorption [36, 37]. From this point of

#### **Figure 2.**

*Elemental composition and FESEM analysis of (a) Matamba biochar (MTBBC), (b) Mupane tree bark (MPNBC), and (c) Mushuma tree bark (MSHBC).*

view, it can be inferred that both conventional and Bayesian approaches to estimations are well established and seem hard to justify if one of the two is preferred over the other [38, 39]. It is thought that the π-π electron donor-acceptor (EDA) interaction is the main player with a major role in the iodine - adsorbent interaction since the adsorption capacity after 2 days of investigation. The strong interaction of π-donor and π-acceptor compounds full fills the EDA theory taking into consideration the FTIR results. As given in **Figure 4e**, the biochar materials also show various surface functional groups. Regarding **Figure 4e**, the peaks at 3334 cm<sup>1</sup> and 1764–1710 cm<sup>1</sup> , can be ascribed to the hydroxyl groups (-OH) and the carbonyl groups (C=O) correspondingly. The shallow peak at 1385 cm<sup>1</sup> and deep and wide peak at 1568 cm<sup>1</sup> are due to C=C stretches of aromatic rings. Furthermore, the 1223 cm<sup>1</sup> peak can be ascribed to the C=O stretching in ethers, alcohols, and/or phenols. The FTIR outcomes clarify that the condition of pyrolysis has a great impact on the adsorption capacity of Iodine in terms of the hydrogen bond capacity created on the biochar materials.

Furthermore, the hydrophobic sites could be originated from the graphitic structure of biochar which is assumed to be interacting with hydrophobic molecules of the biochar. However, the adsorption isotherm results can corroborate this phenomenon. For the tree bark materials, the adsorption kinetic results are shown in **Figure 3c** and **d** and **Table 3** and **Table 3** revealed that the kinetic model fits follow the order PSO > Elovich > PFO > IPD yet for the Matamba fruit shell, Elovich model fitted the adsorption data better than other kinetic models.

*Applications and Data Analysis Using Bayesian and Conventional Statistics in Biochar… DOI: http://dx.doi.org/10.5772/intechopen.105868*

**Figure 3.** *The results from the kinetic adsorption experimental model analysis of biochar materials (a), (c), (d) are PFO, PSO, and Elovich models respectively, and (b), (e) are IPD models correspondingly.*

#### **4.2 Langmuir and Freundlich isotherms on Matamba fruit shell**

Langmuir and Freundlich isotherm models (Eq. (5)) and (Eq. (6)) were used to examine and investigate the adsorption mechanisms of iodine onto the biochar surface. The Langmuir model described well the removal of iodine with the AICc of 0.527 (lower than 5.377 of the Freundlich model), which exhibited monolayer sorption on the Biochar surface with determinate indistinguishable adsorption sites.

Additionally, Bayesian statistics exhibited a clear difference between the two models from the ggplot2 since Freundlich (**Figure 4d**) shows a wider prediction band than Langmuir (**Figure 4c**). The maximum capacity of adsorption deliberated from the Langmuir model was so vital in biochar surface area estimation. The Matamba fruit shell biochar surface area was estimated to be 267.9 m<sup>2</sup> g�<sup>1</sup> and 267.6 m<sup>2</sup> g�<sup>1</sup> from NLS and Bayesian approaches respectively. The biochar surface area was estimated from Iodine adsorption using (Eq. (7)), whereas the Langmuir and Freundlich models reiterate that:

$$\mathbf{q}\_{\mathbf{e}} = \frac{\mathbf{q}\_{m} \,\mathrm{k}\_{\mathrm{L}} \mathbf{c}\_{\mathrm{e}}}{\mathbf{1} + \mathbf{k}\_{\mathrm{L}} \mathbf{c}\_{\mathrm{e}}} \tag{1}$$

$$\mathbf{q\_e} = \mathbf{k\_f c\_e^{mf}} \tag{2}$$

$$\text{SAr} = \text{qt} \ast \text{10} - \text{\\$} \ast \text{NA} \ast \text{oI} \tag{3}$$

#### **Figure 4.**

*(a) Freundlich and (b) Langmuir model density curves, (c) 95% Bayesian C.I. analysis of Langmuir and (d) Freundlich adsorption models, (e) FTIR and FESEM for Matamba biochar, (f) Langmuir and Freundlich adsorption models with conventional analysis method.*

Where q*m* is the maximum adsorption capacity (mmol g<sup>1</sup> ), kL is Langmuir constant (L mmol<sup>1</sup> , Ce is the equilibrium concentration (mmol L<sup>1</sup> ), kf (mmol L mmol<sup>1</sup> ), and mf are Freundlich constants. From this qt is the maximum capacity of adsorption at equilibrium mmol/g, NA is the Avogadro constant, NA = 6.023 <sup>10</sup><sup>23</sup> mol<sup>1</sup> , and ω<sup>I</sup> is the surface area occupied by one iodine molecule (0.2096 \* 10<sup>18</sup> m2 ).

The surface area estimated from both Bayesian and Conventional statistics is insignificant since the *qmax* parameter (**Figure 5a**–**d**) only underscores the capacity of the biochar to adsorb the adsorbate yet has less substantial than the *KL* as explained by [40]. The high value of the *KL* parameter from the MCMC in **Figure 5** is directly proportional to the observed surface area because iodine molecules are small enough and strong to be attached to the biochar surface with minimum effects of desorption.

#### **4.3 Statistical analysis using a Bayesian framework**

The Bayesian statistics obtains *qmax* of 2.12 mmol g<sup>1</sup> and Conventional statistics resulted in the maximum adsorption capacity (*qmax*) of 2.122 mmol g<sup>1</sup> . Moreover, the median value was estimated to be 2.12 mmol g<sup>1</sup> , whereas the MAP value of 2.11 mmol g<sup>1</sup> was obtained and there were no significant differences with the *qmax*. The Rhat between 1.05 and 0.9 is acceptable and helps in the rejection of the Markov

*Applications and Data Analysis Using Bayesian and Conventional Statistics in Biochar… DOI: http://dx.doi.org/10.5772/intechopen.105868*

**Figure 5.**

*Shows the MCMC density presentation of MPNBC and MSHBC from the Bayesian simulation of PSO and Elovich model's posterior probability distribution mean parameters.*

chain Monte Carlo (MCMC) data simulation as it is far away from this range. The Bayesian statistics estimated that the energy binding strength (*KL*) to be higher than the NLS, this is shown in **Table 2** where 218.5 L mmol<sup>1</sup> and 206.43 L mmol<sup>1</sup> were observed for the Bayesian and NLS observed respectively.

The *KL* results exhibited a stronger evaluation as depicted by the Bayesian method than conventional statistics, so, Bayesian statistics seem to have a great capacity to estimate isotherm and kinetics parameters with consistency and supporting evidence than the former. The *KL* is more significant as estimated by the Bayesian analysis and designated the degree of interface among iodine solution and the biochar surface property. Higher values of the *KL* relatively resemble a strong interaction or sorption affinity of the adsorbate concentration onto the adsorbent as large values of *KL* reflect the greater force of binding on the biochar material's surface [41, 42].

#### **5. Conclusions**

The pyrolysis condition at 600°C revealed the surface characteristics and adsorption mechanisms of the biochar materials to be sufficient in generating adequate biochar for the purpose. These agro-biomass materials used in this study were the first to be investigated for their potential application as low-cost adsorbents in rural areas of Zimbabwe for environmental protection. Easy access to these materials as well as lower production cost makes them fit to solve the water shortages and remove unwanted substances from the environment through adsorption. Elovich and PSO models fitted the data in this study, and this exhibits a heterogeneous surface characteristic of the biochar materials with significant chemisorption mechanisms developed during pyrolysis of the agro-biobased biochar. Bayesian statistical analysis has exhibited slightly higher qt estimations of 40.712 and 41.639 mmol/g when compared to the conventional statistics with 40.01 and 40.29 mmol/g for Mushuma and Mupane biochar. The Elovich model subsequently described the results very well, henceforth representing a heterogenous surface property with chemisorption phenomena. FESEM-EDX Spectroscopy also revealed that C (81.93 mol% and 86.91 mol %) and O (16.12 mol% and 11.49 mol%) for Mushuma and Mupane respectively. These percentages agreed with the FTIR results where the surface physical properties designated a rich surface with fundamental functional groups and, as a recommendation with the cost for future research, activating these materials could make them enduring adsorbents. The investigation outcomes unveiled the competence and potential of the locally obtainable and produced biochar in removing Iodine solution as affordable materials that can be established for other emerging contaminants and unwanted pollutants from the environment as water reuse and recycling strategy in developing countries and unserved communities and as a climate change mitigatory measure. Matamba, Museum, and Mupane biochar materials are locally available, no costs are required to obtain them, and the benefits of wastewater recycling strategy should be adopted with a proper design fit for rural communities as off-the-grid technology.

#### **Acknowledgements**

The authors would like to thank Nagasaki University, Graduate School of Engineering, for the provision of resources, chemicals, and analytical equipment that made this work a success. The authors appreciate the referees, editors, and reviewers for their effort in their correction and suggestions to improve the quality and content of this paper.

#### **Conflict of interest**

The authors declare that they have no known competing interests.

#### **Notes/thanks/other declarations**

Most acknowledgment is given to the JICA and HONJO Scholarship Foundation for the provision of these Scholarships to be in Japan for the study period.

*Applications and Data Analysis Using Bayesian and Conventional Statistics in Biochar… DOI: http://dx.doi.org/10.5772/intechopen.105868*

#### **Author details**

Obey Gotore<sup>1</sup> \*, Tirivashe Phillip Masere<sup>2</sup> , Osamu Nakagoe<sup>1</sup> , Vadzanayi Mushayi<sup>3</sup> , Ramaraj Rameshprabu<sup>4</sup> , Yuwalee Unpaprom<sup>4</sup> and Tomoaki Itayama<sup>1</sup>

1 Nagasaki University, Nagasaki, Japan

2 Midlands State University, Gweru, Zimbabwe

3 Harare Polytechnic College, Harare, Zimbabwe

4 Maejo University, Chiang Mai, Thailand

\*Address all correspondence to: gotoreobey@gmail.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

[1] Abedin M, Collins AE, Habiba U, Shaw R. Climate change, water scarcity, and health adaptation in southwestern coastal Bangladesh. International Journal of Disaster Risk Science. 2019;**10**(1): 28-42. DOI: 10.1007/s13753-018-0211-8

[2] Hanna R, Oliva P. Implications of climate change for children in developing countries. The Future of Children. 2016;**26**:115-132. DOI: 10.1353/ foc.2016.0006

[3] Liu X, Tian J, Li Y, Sun N, Mi S, Xie Y, et al. Enhanced dyes adsorption from wastewater via Fe3O4 nanoparticles functionalized activated carbon. Journal of Hazardous Materials. 2019;**373**: 397-407. DOI: 10.1016/j. jhazmat.2019.03.103

[4] Bekçi Z, Özveri C, Seki Y, Yurdakoç K. Sorption of malachite green on chitosan bead. Journal of Hazardous Materials. 2008;**154**(1–3):254-261. DOI: 10.1016/j.jhazmat.2007.10.021

[5] Zhou L, Liu Y, Liu S, Yin Y, Zeng G, Tan X, et al. Investigation of the adsorption-reduction mechanisms of hexavalent chromium by ramie biochars of different pyrolytic temperatures. Bioresource Technology. 2016;**218**: 351-359. DOI: 10.1016/j. biortech.2016.06.102

[6] Manea YK, Khan AM, Wani AA, Saleh MA, Qashqoosh MT, Shahadat M, et al. In-grown flower-like Al-Li/Th-LDH@ CNT nanocomposite for enhanced photocatalytic degradation of MG dye and selective adsorption of Cr (VI). Journal of Environmental Chemical Engineering. 2022;**10**(1):106848. DOI: 10.1016/j.jece.2021.106848

[7] Ghatge S, Yang Y, Ko Y, Yoon Y, Ahn JH, Kim JJ, et al. Degradation of sulfonated polyethylene by a bio-photo-Fenton approach using glucose oxidase immobilized on titanium dioxide. Journal of Hazardous Materials. 2022; **423**:127067. DOI: 10.1016/j. jhazmat.2021.127067

[8] Kodavatiganti S, Bhat AP, Gogate PR. Intensified degradation of acid violet 7 dye using ultrasound combined with hydrogen peroxide, Fenton, and persulfate. Separation and Purification Technology. 2021;**279**:119673. DOI: 10.1016/j.seppur.2021.119673

[9] Gotore O, Rameshprabu R, Itayama T. Adsorption performances of corn cob-derived biochar in saturated and semi-saturated vertical-flow constructed wetlands for nutrient removal under erratic oxygen supply. Environmental Chemistry and Ecotoxicology. 2022;**4**:155-163. DOI: 10.1016/j.enceco.2022.05.001

[10] Prajapati AK, Mondal MK. Comprehensive kinetic and mass transfer modeling for methylene blue dye adsorption onto CuO nanoparticles loaded on nanoporous activated carbon prepared from waste coconut shell. Journal of Molecular Liquids. 2020;**307**: 112949. DOI: 10.1016/j. molliq.2020.112949

[11] Obey G, Adelaide M, Ramaraj R. Biochar derived from non-customized Matamba fruit shell as an adsorbent for wastewater treatment. Journal of Bioresources and Bioproducts. 2022;**7**(2): 109-115. DOI: 10.1016/j. jobab.2021.12.001

[12] Castiglioni M, Rivoira L, Ingrando I, Meucci L, Binetti R, Fungi M, et al. Biochars intended for water filtration: A comparative study with activated

*Applications and Data Analysis Using Bayesian and Conventional Statistics in Biochar… DOI: http://dx.doi.org/10.5772/intechopen.105868*

carbons of their physicochemical properties and removal efficiency towards neutral and anionic organic pollutants. Chemosphere. 2022;**288**: 132538. DOI: 10.3390/en14248472

[13] Lesaoana M, Mlaba RP, Mtunzi FM, Klink MJ, Ejidike P, Pakade VE. Influence of inorganic acid modification on Cr (VI) adsorption performance and the physicochemical properties of activated carbon. South African Journal of Chemical Engineering. 2019;**28**:8-18. DOI: 10.1016/j.sajce.2019.01.001

[14] Cong L, Feng L, Wei X, Jin J, Wu K. Study on the adsorption characteristics of Congo red by sycamore bark activated carbon. In: ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference. Republic of Latvia: The Scientific Journal of Rezekne Academy of Technologies; Vol. 1. 2017. pp. 64-69. DOI: 10.17770/ etr2017vol1.2600

[15] Gautam UK, Panchakarla LS, Dierre B, Fang X, Bando Y, Sekiguchi T, et al. Solvothermal synthesis, cathodoluminescence, and fieldemission properties of pure and N-doped ZnO nano bullets. Advanced Functional Materials. 2009;**19**(1):131-140. DOI: 10.1002/adfm.200801259

[16] Talha MA, Goswami M, Giri BS, Sharma A, Rai BN, Singh RS. Bioremediation of Congo red dye in im7obilized batch and continuous packed bed bioreactor by Brevibacillusparabrevis using coconut shell bio-char. Bioresource Technology. 2018;**252**:37-43. DOI: 10.1016/j. biortech.2017.12.081

[17] Fu MM, Mo CH, Li H, Zhang YN, Huang WX, Wong MH. Comparison of physicochemical properties of biochars

and hydrochars produced from food wastes. Journal of Cleaner Production. 2019;**236**:117637. DOI: 10.1016/j. jclepro.2019.117637

[18] Sriburi T, Wijitkosum S. Biochar amendment experiments in Thailand: Practical examples. In: Bruckman VJ, Klinglmüller M, editors. Potentials to Mitigate Climate Change Using Biochar. Cambridge, United Kingdom: Cambridge University Press; 2016

[19] O'Connor D, Peng TY, Zhang JL, Tsang DCW, Alessi DS, Shen ZT, et al. Biochar application for the remediation of heavy metal polluted land: A review of *in situ* field trials. Science of the Total Environment. 2018;**619**(620):815-826. DOI: 10.1016/j.scitotenv.2017.11.132

[20] Chandra S, Bhattacharya J. Influence of temperature and duration of pyrolysis on the property heterogeneity of rice straw biochar and optimization of pyrolysis conditions for its application in soils. Journal of Cleaner Production. 2019;**215**:1123-1139. DOI: 10.1016/j. jclepro.2019.01.079

[21] Kambo HS, Dutta A. A comparative review of biochar and hydrochar in terms of production, physico-chemical properties and applications. Renewable and Sustainable Energy Reviews. 2015; **45**:359-378. DOI: 10.1016/j. rser.2015.01.050

[22] Gotore O, Osamu N, Rameshprabu R, Arthi M, Unpaprom Y, Itayama T. Iodine adsorption isotherms on Matamba fruit shell stemmed biochar for wastewater re-use strategy in rural areas owing to climate change. Chemosphere. 2022;**303**:135126. DOI: 10.1016/j. chemosphere.2022.135126

[23] Morgan TJ, Turn SQ, George A. Fast pyrolysis behavior of banagrass as a

function of temperature and volatiles residence time in a fluidized bed reactor. PLoS One. 2015;**10**:e0136511. DOI: 10.1371/journal.pone.0136511

[24] Ngadze RT, Verkerk R, Nyanga LK, Fogliano V, Linnemann AR. Improvement of traditional processing of local monkey orange (Strychnos spp.) fruits to enhance nutrition security in Zimbabwe. Food Security. 2017;**9**:1-13. DOI: 10.1007/ s12571-017-0679-x

[25] Mwamba CK. Monkey Orange: Strychnos cocculoides Crops for the Future. Vol. 8. Southampton Centre for Underutilised Crops, UK; 2006

[26] National Research Council. Lost crops of Africa. In: Fruits. Washington DC: The National Academies Press; 2008

[27] Mwampamba TH, Owen M, Pigaht M. Opportunities, challenges and way forward for the charcoal briquette industry in sub-Saharan Africa. Energy for Sustainable Development. 2013;**17**:158-170. DOI: 10.1016/j. esd.2012.10.006

[28] Ajayi OC, Mafongoya PL. Indigenous knowledge systems and climate change management in Africa. Africa Report; Technical Center for Agricultural and Rural Cooperation; 2017. Available online: https://scholar.google.com/ scholar\_lookup?hl=en&publication\_ year=2017&author=P.L.+Mafongoya& author=O.C.+Ajayi&title=Indigenous +Knowledge+Systems+and+Climate +Change+Management+in+Africa

[29] Gotore O, Mushayi V,

Rameshprabu R, Gochayi L, Itayama T. Adsorption studies of iodine removal by low-cost bioinspired Mushuma and Mupane bark derived adsorbents for urban and rural wastewater reuse. International Journal of Human Capital in Urban Management. 2022;**7**(3):

297-308. DOI: 10.22034/ IJHCUM.2022.03.01

[30] Taha SM, Amer ME, Elmarsafy AE, Elkady MY. Adsorption of 15 different pesticides on untreated and phosphoric acid treated biochar and charcoal from water. Journal of Environmental Chemical Engineering. 2014;**2**:20-25. DOI: 10.1016/j.jece.2014.09.001

[31] Hevira L, Ighalo JO, Aziz H, Zein R. Terminalia catappa shell as low-cost biosorbent for the removal of methylene blue from aqueous solutions. Journal of Industrial and Engineering Chemistry. 2021;**97**:188-199. DOI: 10.1016/j. jiec.2021.01.028

[32] Zazycki MA, Godinho M, Perondi D, Foletto EL, Collazzo GC, Dotto GL. New biochar from pecan nutshells as an alternative adsorbent for removing reactive red 141 from aqueous solutions. Journal of Cleaner Production. 2018;**171**: 57-65. DOI: 10.1016/j. jclepro.2017.10.007

[33] Dotto GL, Pinto LA. Adsorption of food dyes onto chitosan: Optimization process and kinetic. Carbohydrate Polymers. 2011;**84**(1):231-238. DOI: 10.1016/j.carbpol.2010.11.028

[34] Cheung CW, Porter JF, McKay G. Elovich equation and modified secondorder equation for sorption of cadmium ions onto bone char. Journal of Chemical Technology & Biotechnology. 2000; **75**(11):963-970. DOI: 10.1002/ 1097-4660(200011)75:11<963::AID-JCTB302>3.0.CO;2-Z

[35] Dang BT, Gotore O, Ramaraj R, Unpaprom Y, Whangchai N, Bui XT, et al. Sustainability and application of corncob-derived biochar for removal of fluoroquinolones. Biomass Conversion and Biorefinery. 2022;**12**(3):913-923. DOI: 10.1007/s13399-020-01222-x

*Applications and Data Analysis Using Bayesian and Conventional Statistics in Biochar… DOI: http://dx.doi.org/10.5772/intechopen.105868*

[36] Doumer ME, Arízaga GG, da Silva DA, Yamamoto CI, Novotny EH, Santos JM, et al. Slow pyrolysis of different Brazilian waste biomasses as sources of soil conditioners and energy, and for environmental protection. Journal of Analytical and Applied Pyrolysis. 2015;**113**:434-443. DOI: 10.1016/j.jaap.2015.03.006

[37] Al-Wabel MI, Al-Omran A, El-Naggar AH, Nadeem M, Usman AR. Pyrolysis temperature induced changes in characteristics and chemical composition of biochar produced from conocarpus wastes. Bioresource Technology. 2013;**131**:374-379. DOI: 10.1016/j.biortech.2012.12.165

[38] Furia CA, Feldt R, Torkar R. Bayesian data analysis in empirical software engineering research. IEEE Transactions on Software Engineering. 2019;**47**(9):1786-1810. DOI: 10.1109/ TSE.2019.2935974

[39] Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian Data Analysis Chapman & Hall. Boca Raton, FL, USA: CRC Texts in Statistical Science; 2004

[40] MacDermid-Watts K, Pradhan R, Dutta A. Catalytic hydrothermal carbonization treatment of biomass for enhanced activated carbon: A review. Waste and Biomass Valorization. 2021; **12**(5):2171-2186. DOI: 10.1007/ s12649-020-01134-x

[41] Bandara T, Xu J, Potter ID, Franks A, Chathurika JB, Tang C. Mechanisms for the removal of Cd (II) and Cu (II) from aqueous solution and mine water by biochars derived from agricultural wastes. Chemosphere. 2020;**254**:126745. DOI: 10.1016/j.chemosphere.2020. 126745

[42] Devi P, Saroha AK. Improvement in performance of sludge-based adsorbents by controlling key parameters by activation/modification: A critical review. Critical Reviews in Environmental Science and Technology. 2016;**46**(21–22):1704-1743. DOI: 10.1080/10643389.2016.1260902

## PAHs, PCBs and Environmental Contamination in Char Products

*Karl Williams, Ala Khodier and Peter Bentley*

#### **Abstract**

Biochar can have unique benefits to terrestrial and aquatic ecosystems. Investigations of biochar effectiveness within these environments often come from homogenous feedstocks, such as plant biomass, which have simple thermochemical processing methods and produce physically and chemically stable biochar. Current methods to increase biochar production include the addition of oil-derived products such as plastics, which produces a more heterogenous feedstock. This feedstock is similar to materials from waste recycling streams. The adoption of more heterogenous feedstocks produces additional challenges to biochar production and use. This can result in pollution contained within the feedstock being transferred to the biochar or the creation of pollutants during the processing. With the current climate emergency, it is essential to eliminate environmental contamination arising from biochar production. It is critical to understand the physiochemical composition of biochar, where detailed analysis of contaminants is often overlooked. Contamination is common from heterogenous feedstocks but on commercial scales, even homogeneous biochar will contain organic pollutants. This chapter investigates biochar produced from various waste feedstocks and the challenges faced in thermochemical processing. Using Automotive Shredder Residue (ASR) as an example of a heterogeneous feedstock, the levels of contamination are explored. Potential solutions are reviewed while assessing the environmental and economic benefits of using biochar from mixed sources.

**Keywords:** persistent organic pollutants, heterogeneous feedstock pyrolysis, biochar secondary processing, automotive shredder residue

#### **1. Introduction**

Biochar has been promoted as a solution to enhance soils as a conditioner and as an additive to enhance contaminated land remediation. For many of these proposed applications the positive properties of the biochar in the environment are championed, however, there is little investigation into their negative impacts on the environment. The main area of concern is the presence of persistent organic pollutants (POP), polyaromatic hydrocarbons (PAH) and polychlorinated biphenyl (PCB) within the biochar itself. Much of the research on the sources of material for biochar is carried out on small scale laboratory test rigs with carefully chosen homogenous feed sources. This does not represent the potential commercial application where a more heterogeneous feed would be present. There is also a drive to enhance and

improve the production of biochar by the combination of organic and plastics. This again can give rise to contamination with undesirable by-products.

It is well known that soils already contain POPS however, there are concerns over these levels [1]. The addition of biochar containing POPs would increase the concentration. The main barrier to analysis of POP in soil is the variability of the soil and a methodology is complex and there is no specified guidelines and corresponding legislation [2]. Consequently, there is no incentive to analyze for organic contamination and the main analysis reports metal levels. For the threshold levels of POP in soil under UK legislation a risk assessment-based approach is required [3].

Many research projects investigating biochar from plant biomass assess the chemical status via the evaluation of organic elemental composition and the biochar porosity only [4, 5]. Although this is a useful method to understand how the char will develop in soil and its potential to absorb nutrients, further analysis of the inorganic metal concentration and the organic pollutants (such as PAHs) contained within the product may provide further information on the environmental contamination from the feedstock that is being added to the soil. Currently, there are limited regulations surrounding biochar reuse from organic products such as biomass, as it is assumed that plants are inert. However, bio-uptake from energy crops contaminated land sources, such as miscanthus, could be a result in a significant amount of pollution retained within biochar following thermal processing. Advanced chemical analysis of biochar is required to ensure that pollution from initial feedstock sources do not cause further pollution.

Biochar is the solid residue obtained during the thermochemical conversion of biomass in an oxygen limiting environment. Unlike combustible ash residues, biochar is a stable solid, rich in pyrogenic carbon. Biochar resides from biomass feedstock, of which there are 6 main sources: agricultural waste, forestry waste, animal waste, industrial residues, and municipal solid waste. Re-use of biochar from waste materials could have many positive environmental and economic effects for the waste recycling industry, including a reduction in waste to landfill and the provision of a circular economy from waste recycling. However, the chemical consistency of the feedstock can have significant implications on the quality of biochar and its potential re-use in certain applications. The use of non-homogeneous feed stocks such automotive shredder residue (ASR) is a good example of a mixture of organic material with oil derived plastic.

There are many applications of biochar, however it is commonly applied to agricultural systems as a soil improver. Addition of biochar to agroecosystems can have significant benefits to soil properties and plant health [6], where carbon sequestration, water retention, microbial activity and herbicide suppression is increased [7–10], whilst nutrient leaching is decreased [11]. It has been calculated that biochar addition can increase soil organic carbon (SOC) stocks by 29% (13 Mg ha¯1 ) [6]. Biochar can be added to soil via different methods, it can be mixed directly into soil, or used as an additive to other processes, such as compost, manures and fertilizers, where the biochar acts as a carrier for the nutrients. Through biochar applications increasing carbon storage within soil, the carbon footprint caused by thermal processing of biomass waste for energy is reduced [12, 13]. Little investigation has been carried out on the level of POP that come from the processing process and different feedstocks. This omission means that we do not have the full picture on what we are depositing onto the land.

Alongside the addition of biochar to agroecosystems to increase soil fertility and improve crop growth, the adsorbent properties of char make it a useful product for

#### *PAHs, PCBs and Environmental Contamination in Char Products DOI: http://dx.doi.org/10.5772/intechopen.106424*

removal of contaminants in remediated soil sites and in aquatic environments. In soil, biochar can be used to immobilize contaminants such as lead, cadmium, arsenic and atrazine [14–16]. In water, biochar can be used to adsorb and remove metal ions such as cadmium, copper and zinc [17] and phenolic compounds [18]. Biochar can also be used to depollute wastewater, removing ammonia [19], dyes such as methylene blue [20] and toxic heavy metals [21, 22]. The chemical consistency and physical structure of biochar determines the pollutants that it can adsorb, where high aromaticity and porosity increase the sorption of organic contaminants and oxygen-containing functional groups increase the sorption of metals [23]. Feedstock type and pyrolysis conditions can alter the char chemical and physical consistency, which has an impact on its use for depollution. Outside of environmental applications, biochar is often used as an additive in the construction sector; where the porous structure acts as a microfiller within concrete composites [24, 25]. Processing of heterogenous feedstocks to make char as a filler in concrete could increase carbon sequestration and reduce the carbon footprint of the concrete [26]. Biochar can also be added as an asphalt binder, increasing its high temperature performance and its resistance to aging [24]. Biochar from waste can also be added to epoxy resins, used in microelectronic, automotive and aircraft industries [27–31]. The adoption of biochar from more complex heterogenous sources such as municipal solid waste (MSW), contaminated wood and ASR could become a more viable option for the recycling industry. The caveat being that these types of products would retain any hazardous chemicals contained in the char and could pose problems at the end of life.

To use solid waste residues as a biochar for soil modification either depollution of feedstock may be required or that of the produced biochar. This will be required in some cases to meet the environmental requirements set by different governmental organizations [32, 33]. There are three main types of regulated contaminants that concern biochar these are: (i) PAHs, (ii) PCBs and (iii) heavy metals. PAHs is a term used for a large group of compounds which have multiple benzene rings in their chemical structure. PAHs are large compounds which are difficult to degrade in the environment. Many PAHs are non-toxic, yet some PAHs with specific chemical structures are carcinogenic and human exposure should be avoided [34, 35]. Current exposure limits to PAHs set by the UK government are 0.25ng/m3 in air and < 0.2 ppb in water [36]. PCBs is a term used for a group of compounds which have two or more chlorine bonds within their hydrocarbon structure. PCBs are highly toxic and are banned in the UK and Europe [37]. Heavy metals that are regulated include lead, mercury and arsenic [37, 38]. There are different exposure limits set dependent on the location (inhalation, ingestion, skin contact). An example of the more common organic pollutants found within biochars is presented in **Figure 1**.

Char samples are typically analyzed for PAHs and PCB by chemical solvent extraction followed by GC–MS (Gas Chromatography—Mass Spectrometer) were extracted from cone and quartered samples of the ASR and pyrolysis solid residues. A common sample preparation method is ultrasonic-enhanced solvent extraction, based on the EPA 3550 method [39]. An example method for PAH analysis is shown in **Table 1**, where anhydrous sodium sulphate is added to a 5g biochar sample, which is extracted using ultrasonic extraction with a 50:50 mixture of hexane/acetone. In this example (**Table 1**), PAHs, PCBs, TPHs and BTXs were detected using Agilent 7890 and 6890 gas chromatographs, in various configurations.

Biochar produced from the thermal processing of organic solid residues is a growing technology which may be used to enhance processing a depollution of waste. However, waste streams are often a complex heterogenous mixture of material,

#### **Figure 1.**

*The chemical structures of some common (A) PAHs, and (B) PCBs detected in solid residue products.*


*b GC/ECD: gas chromatography equipped with electron capture detector. c*

*GC/FID: gas chromatography equipped with flame ionization detector.*

#### **Table 1.**

*Organic analysis operating conditions. Sourced from ref. [40].*

making thermal processing methodology more complex. This chapter will define thermal processing methods and the effect on production of biochar in complex heterogenous waste streams.

#### **2. Processes**

Solid residues can be processed to produce biochar using two thermal processing methods: pyrolysis and gasification. Pyrolysis is the thermal processing of a material at an elevated temperature (400–1000°C) in the absence of oxygen. Pyrolysis produces three main products: syngas, oil, and biochar. Pyrolysis instruments vary in design with the main differences being in the type of kiln used to heat the feedstock and whether post-pyrolysis the gas is being distilled to remove any oil. Pyrolyser designs are often tailored by the feedstock, industry and components such as condensers and distillation systems can be added. Common pyrolyser designs are presented in **Figure 2**.

In addition to pyrolyser design, its operating parameters (temperature, residence time) can have a significant impact on the end products. The temperature and time that the waste is exposed to heat influences the breakdown of compounds and

#### **Figure 2.**

*Schematic diagrams of pyrolysis reactors used in waste processing. A = bubbling fluidised bed; B = circulating fluidised bed; C = screw reactor; and D = rotating cone reactor. Adapted from Khodier [41]; original source: [42].*

the development and chemical consistency of the end-products. Often a higher temperature (800–1000°C) can increase char and syngas production, where lower temperatures (400–800°C) increase oil production [41]. Lower pyrolysis temperature does result in a reduction in contamination due to lower activation energy for larger compounds. Biochar is often produced as a byproduct, with the energy produced from pyrolysis of feedstock influencing the methodology. Often, this results in higher pyrolysis temperatures, causing pollutants to be contained within the biochar products, which requires clean-up.

Pyrolysis operating parameters can have significant impacts on the quality and yield of biochar. It is widely acknowledged that increased pyrolysis temperature and residence time can reduce the reactivity of the char produced [43, 44]. The effect of pyrolysis temperature (range 500–900°C) on char chemical structure was analyzed by Zhao et al. [45] where the pyrolysis temperature is greater than 700°C there was a significant reduction in the carbonyl groups within the aromatic structure. As there was a corresponding increase in oxygen within quinine compounds. Benzene ring condensation increased at 900°C with char having >6 benzene rings within the carbon structures. This was seen to be lower within the higher temperature chars. However, this reduced the char's chemical volatility and contributed to a larger pore size within the particles. Therefore, biochar produced under lower pyrolysis temperature had increased oxygen content and lower particle size, with the higher temperature biochar had increased chemical stability. This has an impact on what market the biochar can be utilized in and as we will see later the types of organic compounds present within the biochar structure.

Biochars that are lower in chemical reactivity may not be suitable for products within the depollution sector (chemical absorbent in water and air depollution) or as a feedstock for gasification. The chemical structure changes in char with pyrolysis temperature (explained above) has significant effects on the adsorption capability in water systems [46]. Pyrolysis temperatures above 500°C an increase the hydrophobicity of biochar, increasing the sorption of organic pollutants [47]. The reduction in biochar pore size and increase in oxygen content within hydrocarbon compounds in lower pyrolysis temperatures (<500°C) can encourage the sorption of inorganic pollutants from water systems, such as heavy metals [46]. It will also influence the retention of POP within the structure. Optimizing pyrolysis methodology to improve biochar utilization in the environment is crucial to meeting environmental targets. Types of biochar feedstocks and their products from different processing routes is presented in **Table 2**.

Investigations of biochar physical structure is focused on effects of homogenous biochar from biomass [84–87] or plastic feedstocks [28–30, 88]. The influence of cofeedstocks is currently being explored to increase syngas quality (CO:H2 ratio), utilize waste, and improve byproducts, where research is still developing. Current findings suggest that co-feeding biomass with plastic feedstocks could have a synergistic effect on the quality of pyrolysis byproducts, where the lower oxygen concentration in plastic feedstocks lower oxygen concentrations and increase hydrogen and carbon concentration [58, 89–92]. However, most studies focus on the production of bio-oil and there is limited research on the impact on biochar structure and any corresponding update of POPS. Biochar from plastics requires pyrolysis at higher temperatures (900°C) [93] to fully decompose, therefore future research should investigate the effects of co-feeding at higher temperatures to determine the impact on solid residue products. This may reduce the potential for organic pollutants within the biochar structure.


*PAHs, PCBs and Environmental Contamination in Char Products DOI: http://dx.doi.org/10.5772/intechopen.106424*

#### **Table 2.**

*Types of feedstock that can be used in pyrolysis and the various products they can make under specific catalysts.*

Biochar itself is currently being reused as a co-feed back into pyrolysis and gasification systems. Gasification is often conducted at a higher temperature than pyrolysis (800–1200°C) with controlled amounts of oxygen or steam to increase the rate of reaction [94]. Gasification does not produce bio-oil as one of the products. In some gasification systems, biochar is used as the feedstock, so the different techniques can complement one another [95]. Biochar produced at lower pyrolysis temperatures can be re-used back within the gasification system and reduce the activation energy required in syngas production. Biochar has been used as a co-feed for many pyrolysis and gasification feedstocks including biomass [49, 54, 57, 94, 95], sewage sludge and municipal waste [44, 66, 70, 71] and coal [61, 96–98]. Addition of biochar as a co-feed could also enhance the quality of the secondary biochar, causing a reduction in the inorganic components within the material. Recycling biochar back into the system will reduce pyrolysis impacts on waste to landfill. Addition of renewable biomass could improve secondary biochar quality and its effectiveness as a product. However, the impact towards the production of POPs is less understood due to limited research in this area.

A significant challenge for the waste industry is the complexities in processing material, which often results in heterogenous biomass feedstocks and the challenge to produce a usable biochar. This results in material often ending up in landfill. A heterogenous feedstock whose use as a biochar is being explored is ASR. The following section will discuss some of the hurdles of heterogeneous feedstocks and will use ASR as one of the worst-case materials.

#### **3. Heterogeneous biochar feedstock: automotive shredder residue**

To highlight the potential contamination within bio-chars a particular example has been chosen. This example will address some of the worst case for mono-source and mixed source feedstocks, ASR is a heterogenous organic waste produced at the end of the waste recycling process of ELVs (End-of-Life Vehicles). ASR makes up approximately 25% of the components of an ELV and is a mixture of organic biomass (textiles, wood) mixed with other waste (consisting of foams, plastics, fibers, glass and residual metals) [40, 93] (**Figure 3**). Recent ELV legislative targets in the UK and Europe require 95% of an ELV is required to be recycled or recovered by 2030 [99]. Currently ASR is sent to landfill; to meet legislative requirements further recovery or re-use is required therefore, the renewed interest in its conversion into a biochar as a potential processing route.

At present, there are only a small number of investigations into pyrolysis of ASR and its suitability for biochar production. As the trend increases to pyrolysis more heterogenous waste streams there will be an increase in the amounts of biochar which will require an end market. Studies indicated that carbon concentration within heterogeneous feedstocks such ASR char were not affected by temperature. This contrasts with crop-based feedstocks as mentioned in the earlier section [93, 100–103], however, the calorific value of the char did decrease with temperature [93, 103]. This could be caused by chemical structure changes within the char, previously seen in pyrolysis of other feedstocks. Further chemical analysis of carbon molecular structure of ASR pyrolyzed at different temperatures would be required to confirm this. The challenge being the heterogeneous nature of material and sampling errors. With governmental pressure to improve recycling activities and the environmental emergency requiring the elimination of fossil fuel energy production, research into this area is expected to expand over the next decade as more product types of biochar emerge.

*PAHs, PCBs and Environmental Contamination in Char Products DOI: http://dx.doi.org/10.5772/intechopen.106424*

#### **Figure 3.**

*Image of ASR from waste recycling plant. Sourced from ref. [93].*

Some of the key findings from research of ASR pyrolysis suggest that there is a significant effect of processing temperature on char particle size and chemical consistency. The biochar from ASR was produced in a 60 kg per hour pilot scale plant by Khodier and Williams [93]. The chars produced were subject to physio-chemically analysis under different temperature conditions (800–1000°C). Findings indicated that finer char was developed at higher pyrolysis temperature (1000°C), with a higher calorific value and lower oxygen content. The biochar produced at both 800 and 1000°C were separated into 'coarse' and 'fine' particle size fractions (coarse: > 0.1 mm diameter; fine = < 0.1 mm) see **Figure 4**. Therefore, allowing the biochar from different particle sizes having different applications depending on their characteristics. The larger particle sizes could be used in iron sintering [104] and to make H2 through steam activation [69, 83, 91] Lower particle sized char, with its more irregular shape [100], which along with an increased microporosity has higher absorbent properties and would be more useful in environmental applications such as water storage in soils and water purification [105–107].

#### **Figure 4.** *Optical images of coarse (a) and fine (b) char. Source taken from ref. [92].*

Biochar produced through pyrolysis of ASR and other heterogenous materials may have similar positive effects on soil properties and water purification as traditional homogenous feedstocks, however this still has to be proved as research into this area is limited. Recent laboratory studies indicated that coal residue biochar can increase SOC (Soil Organic Carbon) and TN (Total Nitrogen) concentration, when compared with maize biomass biochar, fresh residues and control soil [107], which could enhance crop growth. A supposition could be put forward that this would be true for heterogeneous feedstocks. However, it should be noted that with coal residue biochar no toxic contaminants within the chars (e.g., heavy metal concentration, PAH and PCB concentrations) were not studied or any impacts of leaching. As we will see later there are potential restrictions on the use of biochar produced from pyrolysis of heterogenous materials (such as ASR and waste sources such as contaminated wood etc.), due to the high concentrations of PAHs and dioxins in the char being over governmental limits for agricultural processes [36]. Increased chemical depollution of biochar from ASR and other heterogenous feedstocks will be required before use on land [108, 109].

It was found through more detailed analysis of the coarse and fine char fractions of ASR that there was a clear difference in organic pollutants. The fine particle sized fractions (<0.1 mm) had increased concentrations of PAHs and PCBs, which altered with temperature [40]. In contrast to the coarse char (>0.1 mm) which was determined to be inert with low contamination, (levels reported in **Tables 3** and **4**). There


#### **Table 3.**

*Concentrations of PAHs in fine char (at 800 and 1000 C) from ASR feedstock. Data sourced from ref. [40].*


*PAHs, PCBs and Environmental Contamination in Char Products DOI: http://dx.doi.org/10.5772/intechopen.106424*

#### **Table 4.**

*Concentrations of PCBs (7 congeners) and BTEX in produced fine char fraction (at 800 and 1000°C). Data sourced from ref. [98].*

were significant effects of pyrolysis temperature on the PAH and PCB levels within the fine char component, where PAHs decreased with higher pyrolysis temperature (**Table 3**) and concentrations of PCBs increased (**Table 4**). Further investigations on the effect of temperature on the formation and recreation of compounds from heterogenous mixtures is required to determine the best method to reduce environmental contaminants held within the feedstock through process control. It should be noted that the heterogeneous nature of the feedstock makes process control as the sole solution questionable. A more resilient solution would be secondary processing as an effective method to upgrade the biochar and reduce organic pollutants. The next section will define and evaluate current secondary processing of biochar from heterogenous waste sources.

#### **4. Secondary processes to reduce organic pollutants in biochar from heterogeneous sources**

Environmental contamination within biochar from heterogenous sources limits its use in other applications, therefore is often sent to landfill as hazardous waste [93]. Secondary processing of contaminated biochar could reduce the amount of waste to landfill and enable biochar from heterogenous sources to be used as de-pollutants in contaminated land and water systems. There are many methods to reduce pollutants held within biochar. Based on the example in Section 3 [40], a simple reduction in contamination would be size segregation by sieving. If the biochar was sieved to <0.1 mm particle size, the contaminated fine char could be segregated, and the coarse fraction could be re-used. Size segregation of chars would not fully eliminate waste to landfill, so further secondary processing to clean up finer fractions of biochar would be required.

A common secondary process of biochar is carbonization and activation [110]. Carbonization is where volatile and inorganic components of feedstock are removed through thermal treatment, such as a secondary pyrolysis or calcination. The carbon contained in biochar from the pyrolysis process has a disorganized physical structure. Activation is the upgrading of the carbon porosity to regulate the structure. This is conducted though steam or CO2 activation and the addition or impregnation of a catalyst (such as ZnCl2, H3PO4 or KOH) [68, 111]. Activated carbon is chemically stable, with good conductivity due to its' high surface area and can be used to generate EDLCs (Electrical Double Layer Capacitators) and used in depollution of water due to its high adsorption capacity [112]. However, organic waste containing heterogenous components, such as those from ASR, still produce substantial amounts of pollutants following activation [113, 114], so further post treatment is required. Nitric acid addition can be used to remove inorganic metals, followed by a base to neutralize. Studies suggest that this can significantly improve the conductivity of the EDLC without altering the porosity and char texture [115]. However, acid treatment results in excessive amounts of waste which then requires depolluting [116, 117], so may not be a cost-effective solution. Current research has explored molten salt post treatment as an alternative to acid treatment which removes the metal impurities [118]. Cleaner alternatives to activated carbon production for heterogenous feedstocks is required if this is to be economically viable.

In addition to activation and carbonization, another secondary processing method applied to biochar is magnetic synthesis, which can enhance its use as a water decontamination agent, due to the easy removal from the system postadsorption [119, 120]. The use of Fe3O4 as a catalyst under CO2 can encourage the formation of magnetic biochar (magnetite Fe3O4; saturation magnetization 28.4 emu g−1), which has a high heavy metal adsorption [64]. Magnetization could increase removal of heavy metals from aquatic environments and improve water quality in polluted areas [120]. Impregnation of metal composites such as FeSO4 into heterogeneous feedstock pre-pyrolysis can produce magnetic biochar. It has been found that an iron loading of 8% in the feedstock also enhanced biochar production [48]. Impregnation of iron composites within pyrolysis systems with heterogenous feedstocks, such as ASR, could enhance the utilization of the biochar as a magnetic activated carbon product.

Within heterogenous waste streams, further sorting of material pre-treatment could have significant effects on the contamination found within the biochar product. Using ASR as an example, the elimination of PVC from plastics within the material could significantly reduce the number of PCBs in the final product [93]. In addition to this, improved sorting could reduce the number of contaminants within the biochar, making secondary treatment more effective. Further sorting of the biomass (wood) and polymer (plastics, foams) materials of ASR may improve secondary depollution of biochar [108] and improve production of activated carbon [121]. Certain types of plastic removal from ASR would increasing the homogeneity of the feedstock to be pyrolyzed [122]. Development of feedstock sorting practices is possible; however, this would require significant changes to waste management practices which may not be practical.

If feedstock sorting is not a viable homogenization option, pre-treatment of feedstock by calcination could increase homogenization of the feedstock by reducing the particle size without causing depolymerization of hydrocarbons and devolatilization of plastic components [123]. It should be noted that typical feedstocks have not been tested at larger pilot scales, so it is difficult to evaluate the impact of scaling

on the outputs. Torrefaction may be another suitable method of homogenizing feedstocks without fine metal sorting [124]. The process of calcination is a thermal pre-treatment conducted under limited oxygen, whereas torrefaction is conducted in the absence of oxygen. Further tests are required to determine the differences between torrefaction and calcination on the chemical consistency of the improved heterogenous feedstock to provide information on the optimum conditions. The economic impacts of an extra thermal pre-treatment step on the overall pyrolysis process requires careful evaluation to determine if increase product quality and yield are enough to promote investment.

Another method of reducing POP contamination of biochar is the reprocessing of it back into a thermal process. Biochar can be utilized back within the pyrolysis system to upgrade and clean the syngas, where the absorbent properties of char can increase H2S removal [70] and can improve production of other byproducts such as ethylbenzene [71], where its catalytic properties can crack hydrocarbon chains. Alongside directly altering syngas properties, addition of char as a co-catalyst can increase regeneration of catalysts, improving production costs [57]. Utilization of char in other pyrolysis systems where the feedstock is more oxygenated, such as plant biomass, can have a deoxygenation effect, improving the quality of bio-oil products and increasing the syngas value [125]. Added to syngas systems, biochar can be used to clean up combustion systems by adsorbing CO2 emissions, reducing the negative industrial impact on global warming. Upgrading biochar through addition of metal composites such as Fe2O3 and Al2O3 can increase the adsorption through increasing char surface area and sorption capacity [57]. This is a low-cost CO2 adsorption method, where catalyst desorption and regeneration temperature occurs at 120°C. Reprocessing biochar developed from heterogeneous feedstocks could be a viable option of creating more homogenous products which can be more effectively utilized. If using this approach, it is essential that contaminants within biochar are monitored to ensure that the addition of a catalyst in the gasification process can increase H2 production [126] and limit PAH formation within the biochar [127]. Using Ca/Na compounds as a catalyst reduces the production of aromatic structures and increase the formation of more active intermediates beneficial to gasification [127]. However, research into the effects of biochar as a feedstock for gasification and H2 production is focused on char derived from homogenous feedstocks [59, 96, 97, 126–129]. ASR derived biochar has a more volatile carbonaceous structure than homogenous chars [130], meaning it could be more effective in gasification processes under steam activation. However, it could also be more difficult to select the correct catalyst with a wide range of pollutants present in the char (**Tables 3** and **4**). Further information on the physiochemical structure of ASR-derived biochars and effects of catalyst addition under steam activation is required ensure that depollution of char in this process is effective.

Alternative methods to processing contaminated biochar from heterogenous waste sources is to look at the sequestration of this product in composite materials. This will be in areas where the physical structure of the biochar improves the physical composition of the material and the pollutants are contained; reducing their effects. Containing polluted char within concrete may be a sustainable method for their use whilst at the same time reducing natural resource depletion from production concrete materials. Although, there is significant potential to utilize char in concrete materials, where an increased cement hydration and the immobilization of contaminants has been determined, there is still significant research required before commercial products can be manufactured and sold. The effect of char particle size,

feedstock type and dosing amount can influence the tensile strength of the concrete, where if not correct, micro cracking can be caused [131]. Caeteno et al., [132] highlighted how finer fractions of heterogeneous bio char (ASR) when added to concrete had a beneficial effect. From the earlier sections highlighting that POPs were associated with certain size fractions for biochar from ASR sieved finer char could be used as a concrete agent and the inert coarser biochar for other applications. However, size separation of bulk material could be a time-consuming and expensive process.

A significant limiting factor of research into biochar production and its' secondary processing is the lack of pilot scale projects. Many initial pyrolysis trials of biochar production and applications were conducted at a laboratory-scale (**Table 2**). Upscaling of laboratory scale to pilot scale systems is required to increase accuracy in the effectiveness of a catalyst on product yield and quality. To gain accurate results from a laboratory scale experiment a large amount of replication is required due to the small sample size (often 1-10 g feedstock), where upscaling to a pilot reactor can process 1000× more material, providing more realistic results. This would benefit heterogenous feedstocks by reducing error in sampling due to missing of potential contamination. This is also true of homogeneous feedstocks with added plastic material to improve yield. The next step in the development of products from heterogeneous feedstocks such as ASR will be to test effective catalysts on a pilot scale reactor. This would provide more accurate information on the effects of catalysis on a commercial scale, improving depollution of complex feedstocks. Due to the heterogeneity of the material, more than one catalyst may be required to target specific components of the feedstock. Two-stage catalysis of plastics has been investigated [133, 134], which might be a viable option for heterogenous biochar production.

#### **5. Economic and environmental impacts driving biochar depollution**

Producing biochar from heterogenous feedstocks and the potential contamination from POPs will be decided by two conflicting economical drivers: (i) whether biochar is being developed to stop feedstock going to landfill, or (ii) whether biochar is being produced for a specific application (such as activated carbon). If reducing material to landfill was the business focus, then biochar production from heterogenous sources (such as ASR) will be the driver for secondary processing development. Although secondary processing of biochar will reduce environmental contamination, the energy and resources required to implement these changes may outweigh other costs, such as landfill. If it is to produce biochar for specific applications then pre-processing technology would be the focus. In future, plastic components within waste streams including ASR will be classed as hazardous [40]. Many plastics already contain significant amounts of POPs, increasing the cost of landfill tax and the expense of disposal [3, 135], (EU Regulation on persistent organic pollutants (2019/1021) was adopted on 20 June 2019). This may lead to unforeseen consequences where businesses are making biochars that cannot be used because of elevated levels of contamination. The economics of processes will lead to a trade-off between reduction in waste to landfill and the creation of contaminated char with or without secondary processing. An analogous situation also arises with the use of crops being used to depollute contaminated soil systems and then used for energy. The biochar produced will contain pollutants which is then spreading contaminated biochar as a conditioner. This section

#### *PAHs, PCBs and Environmental Contamination in Char Products DOI: http://dx.doi.org/10.5772/intechopen.106424*

will assess the economic and environmental costs to a waste recycling business when introducing thermal processing systems and the challenges and opportunities faced during commercialization.

Waste biochar produced from heterogenous sources such as ASR and MSW can produce a circular economy from waste streams [136–138]. In addition to the environmental incentive of reducing waste to landfill, a reduction in landfill tax is a significant economic opportunity, where current UK rates are £98.60 per tonne [139]. However, the chemical contamination within biochar (**Tables 3** and **4**) means that biochar from certain waste streams (such as ASR) could be classified as hazardous [40] which would increase landfilling costs. This could deter waste recycling industries from investing in biochar production, where a large financial investment is already required upon the purchase of a pyrolysis plant (**Table 5**) Upgraded biochar from secondary processing methods could produce a viable product that would promote a circular economy. However, the addition in business costs from development and maintenance of a secondary processing system might outweigh the costs of landfilling contaminated biochars. Long-term lifecycle assessment studies are required to investigate the payback and carbon/energy balances of these systems, which will determine whether secondary processing is an appropriate method in the future. There is no simple solution, and we are potentially creating legacy problems for the future.


#### **Table 5.**

*Summary of reported pyrolysis plant cost. Sourced from ref. [39].*

#### **6. Conclusion**

Production of biochar from heterogenous materials is likely to increase over the next decade as governments attempt to reach environmental targets for 2030 following COP26. The use of waste biomass for energy sources will be a driver in future energy production as the world resorts towards cleaner energy and away from fossil fuels. As highlighted in this chapter, utilization of biochar produced from thermal recycling of heterogenous waste feedstocks pose many challenges due to prominent levels of POPs and heavy metals within the feedstock. The amounts and types of persistent organic present is discussed. Secondary processing is a potential solution to remove contamination from biochars but the economics and readiness for the market are currently the limiting factors. Future opportunities to upgrade biochar through secondary processing are being adopted within the sector but are yet to be commercially available.

### **Acknowledgements**

The authors gratefully acknowledge the following financial support: Higher Education Innovation Fund UKRI, Innovate UK's support through the Knowledge Transfer Partnership (KTP). As well as both the University of Central Lancashire and Recycling Lives Limited, Preston, UK for their financial support and access to their facilities.

#### **Conflict of interest**

The authors declare no conflict of interest.

#### **Author details**

Karl Williams\*, Ala Khodier and Peter Bentley University of Central Lancashire, Preston, UK

\*Address all correspondence to: kswilliams@uclan.ac.uk

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### **References**

[1] Nam JJ, Thomas GO, Jaward FM, Steinnes E, Gustafsson O, Jones KC. PAHs in background soils from Western Europe: Influence of atmospheric deposition and soil organic matter. Chemosphere (Oxford). 2008;**70**(9):1596-1602. DOI: 10.1016/j. chemosphere.2007.08.010

[2] Kalbe U, Lehnik-Habrink P, Bandow N, Sauer A. Validation of European horizontal methods for the analysis of PAH, PCB and dioxins in sludge, treated biowaste and soil. Environmental Sciences Europe. 2019;**31**:1. DOI: 10.1186/ s12302-019-0211-3

[3] UK Government. 2017. Landfill Tax rates for 2022 to 2023 . Landfill Tax rates for 2022 to 2023-GOV.UK. Available from: www.gov.uk

[4] Mimmo T, Panzacchi P, Baratieri M, Davies C, Tonon G. Effect of pyrolysis temperature on miscanthus (Miscanthus × giganteus) biochar physical, chemical and functional properties. Biomass & Bioenergy. 2014;**62**:149-157. DOI: 10.1016/j.biombioe.2014.01.004

[5] Singh A, Nanda S, Guayaquil-Sosa JF, Berruti F. Pyrolysis of Miscanthus and characterization of value-added bio-oil and biochar products. Canadian Journal of Chemical Engineering. 2021;**99**(1):S55-S68. DOI: 10.1002/ cjce.23978

[6] Schulz H, Dunst G, Glaser B. Positive effects of composted biochar on plant growth and soil fertility. Agronomy for Sustainable Development. 2013;**33**(4):817-827

[7] Jones DL, Edwards-Jones G, Murphy DV. Biochar mediated alterations in herbicide breakdown and leaching in soil. Soil Biology and Biochemistry. 2011;**43**:804-813

[8] Quilliam RS, Marsden KA, Gertler CH, Rousk J, De Luca TH, Jones DL. Nutrient dynamics, microbial growth and weed emergence in biochar amended soil are influenced by time since application and reapplication rate. Agriculture, Ecosystems and Environment. 2012;**158**:192-199

[9] Han F, Ren L, Zhang X-C. Effect of biochar on the soil nutrients about different grasslands in the loess plateau. Catena. 2016;**137**:554-562

[10] Raclavská H, Růžičková J, Škrobánková H, Koval S, Kucbel M, Racklavský K, et al. Possibilities of the utilisation of char from the pyrolysis of tetrapak. Journal of Environmental Management. 2018;**219**:231-238

[11] Gross A, Bromm T, Glaser B. Soil organic carbon sequestration after biochar application: A global metaanalysis. Agronomy. 2021;**11**:2474

[12] Smith P. Soil carbon sequestration and biochar as negative emission technologies. Global Change Biology. 2016;**22**:1315-1324

[13] Fuss S, Lamb WF, Callaghan MW, Hilaire J, Creutzig F, Amann T, et al. Negative emissions, part 2: Costs, potentials and side effects. Environmental Research Letters. 2018;**13**:063002

[14] Cao XD, Ma LN, Gao B, Harris W. Dairy manure derived biochar effectively sorbs lead and atrazine. Environmental Science and Technology. 2009;**43**:3285-3291

[15] Cao XD, Ma LN, Liang Y, Gao B, Harris W. Simultaneous immobilization of lead and atrazine in contaminated soils using dairy manure biochar. Environmental Science and Technology. 2011;**45**:4884-4889

[16] Liu L, Xiu L, Wang D, Lin H, Huang L. Removal and reduction of Cr (VI) in simulated waste water using magnetic biochar prepared by co-pyrolysis of nano-zero-valent iron and sewage sludge. Journal of Cleaner Production. 2020;**257**:120562

[17] Bogusz A, Oleszczuk P, Dobrowlowski R. Application of laboratory prepared and commercially available biochars to adsorption of cadmium, copper and zinc ions from water. Bioresource Technology. 2015;**196**:540-549

[18] Kuśmierek K, Świątowski A, Kotkowski T, Cherbański R, Molga E. Adsorption properties of activated tire pyrolysis chars for phenol and chlorophenols. Chemical Engineering and Technology. 2020;**43**(4):770-780

[19] Tang Y, Alam MS, Konhauser KO, Alessi DS, Xu S, Tian W, et al. Influence of pyrolysis temperature on production of digested sludge biochar and its application for ammonium removal from municipal wastewater. Journal of Cleaner Production. 2019;**209**:927-936

[20] Hu M, Deng W, Hu M, Chen G, Zhou P, Zhou Y, et al. Preparation of binder-less activated char briquettes from pyrolysis of sewage sludge for liquid-phase adsorption of methylene blue. Journal of Environmental Management. 2021;**299**:113601

[21] Calugaru LL, Neculita CM, Genty T, Zagury GJ. Removal efficiency of As(V) and Sb(III) in contaminated neutral drainage by Fe-loaded biochar.

Environmental Science and Pollution Research. 2019;**26**:9322-9332

[22] Liu Y, Huang J, Xu H, Zhang Y, Hu T, Chen W, et al. A magnetic macro-porous biochar sphere as vehicle for activation and removal of heavy metals from contaminated agricultural soil. Chemical Engineering Journal. 2020;**390**:124638

[23] Braghiroli FL, Bouafif H, Neculita CM, Koubaa A. Activated biochar as an effective sorbent for organic and inorganic contaminants in water. Water, Air and Soil Pollution. 2018;**229**(7):1-22

[24] Gupta S, Kua HW, Low CY. Use of biochar as a carbon sequestering additive in cement mortar. Cement and Concrete Composites. 2018;**87**:110-129

[25] Cuthbertson D, Berardi M, Briens C, Berruti F. Biochar from residual biomass as a concrete filler for improved thermal and acoustic properties. Biomass and Bioenergy. 2019;**120**:77-83

[26] Wijesekara DA, Sargent P, Ennis CJ, Hughes D. Prospects of using chars derived from mixed post waste plastic pyrolysis in civil engineering applications. Journal of Cleaner Production. 2021;**317**:128212

[27] Kumar A, Choudhary R, Kumar A. Aging characteristics of asphalt binders modified with waste tire and plastic pyrolytic chars. PLoS One. 2021;**16**(8):e0256360

[28] Ahmetli G, Kocaman S, Ozaytekin I, Bozkurt P. Epoxy composites based on inexpensive char filler obtained from plastic waste and natural resources. Polymer Composites. 2013;**34**:500-509

[29] Sogancioglu M, Yel E, Ahmetli G. Pyrolysis of waste high density polyethylene (HDPE) and low density *PAHs, PCBs and Environmental Contamination in Char Products DOI: http://dx.doi.org/10.5772/intechopen.106424*

polyethylene (LDPE) plastics and production of epoxy composites with their pyrolysis chars. Journal of Cleaner Production. 2017;**165**:369-381

[30] Sogancioglu M, Yel E, Ahmetli G. Behaviour of waste polypropylene pyrolysis char-based epoxy composite materials. Environmental Science and Pollution Research. 2020;**27**:3871-3884

[31] Kahrizsangi AG, Shariatpanahi H, Neshati J, Akbarinezhad E. Corrosion behaviour of modified nano carbon black/epoxy coating in accelerated conditions. Applied Surface Science. 2015;**331**:115-126

[32] European Commission. The Environmental Liability Directive, Directive 2004/35/EC. 2004. Liability - Legislation - Environment - European Commission. Available from: europa.eu

[33] Environmental Protection Agency. Environmental Justice Strategy. 1995. Available from: ej\_strategy\_1995.pdf (epa.gov)

[34] Culp SJ, Beland FA. Comparison of DNA (Deoxyribonucleic Acid) Adduct Formation in Mice Fed Coal Tar or Benzo[a]Pyrene. Carcinogenesis (New York). OXFORD: Oxford University Press. 1994;**15**(2):247-252. DOI: 10.1093/ carcin/15.2.247

[35] Culp SJ, Gaylor DW, Sheldon WG, Goldstein LS, Beland FA. A comparison of the tumours induced by coal tar and benzo[a]pyrene in a two-year assay. Carcinogenesis. 1998;**19**(1):117-124. DOI: 10.1093/carcin/19.1.117

[36] European Commission. 1996. Council Directive 96/59/EC of 16 September 1996 on the disposal of polychlorinated biphenyls and polychlorinated terphenyls (PCB/PCT), Official Journal L 243, 24 September

1996. pp. 31-35. Available from: eur39237. doc(live.com)

[37] European Commission. 2008. Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe. Available from: EUR-Lex-32008L0050-EN-EUR-Lex(europa.eu)

[38] European Commission. 2004. Directive 2004/107/EC of the European Parliament and of the council of 15 December 2004 relating to arsenic, cadmium, mercury, nickel and polycyclic aromatic hydrocarbons in ambient air

[39] US EPA. 2007. EPA 3550C, Ultrasonic Extraction, Office of research and development. Available from: www. epa.gov/sites/production/files/2015-12/ documents/3550c.pdf

[40] Williams KS, Khodier A. Meeting EU ELV targets: Pilot-scale pyrolysis automotive shredder residue investigation of PAHs PCBs and environmental contaminants in the solid residue products. Waste Management. 2020;**105**:233-239

[41] Khodier A. Automotive shredder residue (ASR) for clean energy systems (pyrolysis and gasification) to produce sustainable green energy. Masters Thesis, University of Central Lancashire. 2019. Available from: clok.uclan.ac.uk

[42] Ronsse F, Dickinson D, Nachenius R, Prins W. Biomass pyrolysis and biochar characterization. In: Presented at the 1st FOREBIOM workshop. Vienna: Austrian Academy of Sciences; 2013

[43] Tushar MSHK, Mahinpey N, Khan A, Ibrahim H, Kumar P, Idem R. Production, characterization and reactivity of chars produced by the isothermal pyrolysis of flax straw. Biomass and Bioenergy. 2012;**37**:97-105

[44] Wang X, Zhai M, Guo H, Panahi A, Dong P, Levendis YA. High temperature pyrolysis of biomass pellets: The effect of ash melting on char structure. Fuel. 2021;**285**:119084

[45] Zhao Y, Feng D, Zhang Y, Huang Y, Sun S. Effect of pyrolysis temperature on char structure and chemical speciation of alkali and alkaline earth metallic species in biochar. Fuel Processing Technology. 2016;**141**(1):54-60

[46] Enaime G, Baçaoui A, Yaacoubi A, Lübken M. Biochar for wastewater treatment: Conversion technologies and applications. Applied Sciences. 2020;**10**(10):34912

[47] Keiluweit MM, Nico PS, Johnson MG, Kleber M. Dynamic molecular structure of plant biomass derived black carbon (biochar). Environmental Science and Technology. 2010;**44**:1247-1253

[48] Han T, Yang W, Jönsson PG. Pyrolysis and subsequent steam gasification of metal dry impregnated lignin of the production of H2-rich syngas and magnetic activated carbon. Chemical Engineering Journal. 2020;**394**:124902

[49] Waheed QMK, Wu C, Williams PT. Hydrogen production from high temperature steam catalytic gasification of bio-char. Journal of the Energy Institute. 2016;**89**(2):222-230

[50] Ellison CR, Boldor D. Mild upgrading of biomass pyrolysis vapors via ex-situ catalytic pyrolysis over an iron montmorillonite catalyst. Fuel. 2021;**291**:120226

[51] Widyaningrum RN, Church TL, Zhao M, Harris AT. Mesocellularfoam-silica-supported Ni catalyst: Effect of pore size on H2 production from cellular pyrolysis. International Journal of Hydrogen Energy. 2012;**37**(12):9590-9601

[52] Rezaei PS, Shafaghat H, Daud WMAW. Suppression of coke formation and enhancement of aromatic hydrocarbon production in catalytic fast pyrolysis of cellulose over different zeolites: Effects of pore structure and acidity. Royal Society of Chemistry Advances. 2015;**5**:65408-65414

[53] Huynh VN, Dang NT, Truong TT, Van T. Catalytic upgrading and enhancing the combustion characteristic of pyrolysis oil. International Journal of Green Energy. 2021;**18**(12):1277-1288

[54] Zhou Y, Chen Z, Gong H, Wang X, Yu H. A strategy of using recycled char as a co-catalyst in cyclic in-situ catalytic cattle manure pyrolysis for increasing gas production. Waste Management. 2020;**107**:74-81

[55] Singh S, Kumar Bhaumik S, Dong L, Li C-Z, Vuthaluru H. An integrated twostep process of reforming and adsorption using biochar for enhanced tar removal in syngas cleaning. Fuel. 2022;**307**:121935

[56] Williams PT, Horne PA. The influence of catalyst type on the composition of upgraded biomass pyrolysis oils. Journal of Analytical and Applied Pyrolysis. 1995;**31**:39-61

[57] Creamer AE, Gao B, Wang S. Carbon dioxide capture using various metal oxyhydroxide-biochar composites. Chemical Engineering Journal. 2016;**283**:826-832

[58] Tong W, Liu Q, Yang C, Cai Z, Wu H, Ren S. Effect of pore structure on CO2 gasification reactivity of biomass chars under high temperature pyrolysis. Journal of Energy Institute. 2020;**93**(3):962-976

[59] Yan F, Luo S-Y, Hu Z-Q, Cheng G. Hydrogen-rich gas production by steam *PAHs, PCBs and Environmental Contamination in Char Products DOI: http://dx.doi.org/10.5772/intechopen.106424*

gasification of char from biomass fast pyrolysis in a fixed bed reactor: Influence of temperature and steam on hydrogen yield and syngas composition. Bioresource Technology. 2010;**101**(14):5633-5637

[60] Yang S, Zhang X, Chen L, Sun L, Xie X, Zhao B. Production of syngas from pyrolysis of biomass using Fe/CaO catalysts: Effect of operating conditions on the process. Journal of Analytical and Applied Pyrolysis. 2017;**125**:1-8

[61] Wu Y, Yu H, Chao H, Chen D. A novel nickel catalyst supported on activated steel slags for syngas production and tar removal from biomass pyrolysis. International Journal of Hydrogen Energy. 2021;**46**(75):37268-37280

[62] Kwon EE, Jeon EC, Castaldi MJ, Jeon YJ. Effect of carbon dioxide on the thermal degradation of lignocellulosic biomass. Environmental Science and Technology. 2013;**47**:10541-10547

[63] Kwon EE, Cho SH, Kim S. Synergetic sustainability enhancement via utilisation of carbon dioxide as carbon neutral chemical feedstock in the thermo-chemical processing of biomass. Environmental Science and Technology. 2015;**49**:5028-5034

[64] Cho D-W, Kwon G, Yoon K, Tsang YF, Ok YS, Kwon EE, et al. Simultaneous production of syngas and magnetic biochar via pyrolysis of paper mill sludge using CO2 as a reaction medium. Energy Conversion and Management. 2017;**145**:1-9

[65] Rocha LS, Sousa ÉML, Gil MV, Oliveira JABP, Otero M, Esteves VI, et al. Producing magnetic nanocomposites from paper sludge for the adsorptive removal of pharmaceuticals from water—A fractional factorial design. Nanomaterials (Basel, Switzerland).

2021;**11**(2):1-20. DOI: 10.3390/ nano11020287

[66] Wang S, Shan R, Gu J, Zhang J, Yuan H, Chen Y. Pyrolysis of municipal sludge char supported Fe/Ni catalysts for catalytic reforming of tar model compound. Fuel. 2020;**279**:118494

[67] Qin J, Jiao Y, Li X, Liu Y, Lei Y, Gao J. Sludge char-to-fuel approaches based on the catalytic pyrolysis II: Heat release. Environmental Science and Pollution Research International. 2018;**25**(36):36581-36588. DOI: 10.1007/ s11356-018-3596-4

[68] Alvarez J, Lopez G, Amutio M, Bilbao J, Olazar M. Preparation of absorbents from sewage sludge pyrolytic char by carbon dioxide activation. Process Safety and Environmental Protection. 2016;**103**:76-86

[69] Chun YN, Lim MS, Yoshikawa K. Characteristics of the products from steam activation of sewage sludge. Journal of Industrial and Engineering Chemistry. 2012;**18**(2):839-847

[70] Hervy M, Pham M, Gérente C, Weiss-Hortala E, Nzihou A, Villot A, et al. H2S removal from syngas using waste pyrolysis chars. Journal of Chemical Engineering. 2018;**334**:2179-2189

[71] Hervy M, Villot A, Gérrente C, Pham M, Weiss-Hortala E, Nzihou A, et al. Catalytic cracking from ethylbenzene as tar surrogate using pyrolysis chars from wastes. Biomass and Bioenergy. 2018;**117**:86-95

[72] Yao D, Yang H, Chen H, Williams PT. Investigation of nickel-impregnated zeolite catalysts for hydrogen/syngas production from the catalytic reforming of waste polyethylene. Applied Catalysis B: Environmental. 2018;**227**:477-487

[73] Oh D, Lee HW, Kim Y-M, Park Y-K. Catalytic pyrolysis of polystyrene and polyethylene phthalate over Al-MSU-F. Energy Procedia. 2018;**144**:111-117

[74] Lee HW, Park Y-K. Catalytic pyrolysis of polyethylene and polypropylene over desilicated beta and Al-MSU-F. Catalysts. 2018;**8**(501):1-15

[75] Dou B, Wang K, Jiang B, Song Y, Zhang C, Chen H, et al. Fluidized bed gasification combined continuous sorption-enhanced steam reforming system to continuous hydrogen production from waste plastic. International Journal of Hydrogen Energy. 2016;**41**:3803-3810

[76] Sonaware YB, Shindikar MR, Khaladkar MY. High calorific value fuel from household plastic waste by catalytic pyrolysis. Nature, Environment and Pollution Technology. 2017;**16**(3):879-882

[77] Li K, Lei J, Yuan G, Weerachanchai P, Wang J-Y, Zhao J, et al. Fe-, Ti-, Zr-, and Al-pillared clays for efficient catalytic pyrolysis of mixed plastics. Chemical Engineering Journal. 2017;**317**:800-809

[78] Muneer B, Zeeshan M, Qaisar S, Razzaq M, Iftikhar H. Influence of in-situ and ex-situ HZSM-5 catalyst on co-pyrolysis of corn stalk and polystyrene with a focus on liquid yield and quality. Journal of Cleaner Production. 2019;**237**:117762

[79] Xuanjun J, Lee JH, Choi JW. Catalytic co-pyrolysis of woody biomass with waste plastics: Effects of HZSM-5 and pyrolysis temperature on producing high value pyrolytic products and reducing wax formation. Energy. 2022;**235:A**:121739

[80] Jung S, Lee T, Lee J, Lin K-YA, Park Y-K, Kwon EE. Catalytic pyrolysis of plastics derived from end-of-life vehicles (ELVs) under the CO2 environment. International Journal of Energy Research. 2021;**45**(11):16781-16793

[81] Miskolczi N, Juzsakova T, Sója J. Preparation and application of metal loaded ZSM-5 and y-zeolite catalysts for thermo-catalytic pyrolysis of real end of life vehicles plastic waste. Journal of the Energy Institute. 2019;**92**:118-127

[82] Miskolczi N, Sója J, Tulok E. Thermo-catalytic two-step pyrolysis of real waste plastics from end-of-life vehicle. Journal of Analytical and Applied Pyrolysis. 2017;**128**:1-12

[83] Chaudhari ST, Dalai AK, Bakhshi NN. Production of hydrogen and/or syngas (H2 + CO) via steam gasification of biomass-derived chars. Energy & Fuels. 2003;**17**(4):1062-1067

[84] Xiao R, Yang W. Influence of temperature on organic structure of biomass pyrolysis products. Renewable Energy. 2013;**50**:136-141

[85] Guizani C, Jeguirim M, Valin S, Limousy Y, Salvadour S. Biomass chars: The effects of pyrolysis conditions on their morphology, structure, chemical properties and reactivity. Energies. 2017;**10**(6):1-18

[86] Yu J, Sun L, Berrueco C, Fidalgo B, Paterson N, Millian M. Influence of temperature and particle size on structural characteristics of chars from beechwood pyrolysis. Journal of Analytical and Applied Pyrolysis. 2018;**130**:127-134

[87] Huang Y, Liu S, Aktar MA, Li B, Zhou J, Zhang S, et al. Volatile charinteractions using biomass pyrolysis: Understanding the potential origin of char activity. Bioresource Technology. 2020;**316**:123938

*PAHs, PCBs and Environmental Contamination in Char Products DOI: http://dx.doi.org/10.5772/intechopen.106424*

[88] Mastral F, Esperanza E, Garcia P, Juste M. Pyrolysis of highdensity polyethylene in a fluidised bed reactor: Influence of temperature and residence time. Journal of Analytical and Applied Pyrolysis. 2002;**63**:1-15

[89] Al-Rumaihi A, Shahbaz M, McKay G, Mackey H, Al-Ansari T. A review of pyrolysis technologies and feedstock: A blending approach for plastic and biomass towards optimum char yield. Renewable and Sustainable Energy Reviews. 2022;**167**:112715

[90] Ke L, Wu K, Zhou N, Xiong J, Yang Q, Zhang L, et al. Lignocellulosic biomass pyrolysis for aromatic hydrocarbons production: Pre and in-process enhancement methods. Renewable and Sustainable Energy Reviews. 2022;**165**:112607

[91] Vo TA, Tran QK, Ly HV, Kwon B, Hwang HT, Kim J, et al. Co-pyrolysis of lignocellulosic biomass and plastics: A comprehensive study on pyrolysis kinetics and characteristics. Journal of Analytical and Applied Pyrolysis. 2022;**163**:105464. DOI: 10.1016/j. jaap.2022.105464

[92] Likun Z, H., & Fan, Y. Improving hydrocarbons production via catalytic co-pyrolysis of torrefied-biomass with plastics and dual catalytic pyrolysis. Chinese Journal of Chemical Engineering. 2022;**42**:196-209. DOI: 10.1016/j.cjche.2020.09.074

[93] Khodier A, Williams KS, Dallison N. Pilot-scale thermal treatment of automotive shredder residue: Pyrolysis char is a resource or waste. WIT Transactions on Ecology and the Environment. 2017;**224**(1):439-450

[94] Mahinpey N, Gomez A. Review of gasification fundamentals and new findings: Reactors, feedstock, and kinetic studies. Chemical Engineering Science. 2016;**148**:14-31

[95] Esmaeili V, Ajalli J, Faramarzi A, Abdi M, Gholizadeh M. Gasification of wastes: The impact of the feedstock type and co-gasification on the formation of volatiles and char. International Journal of Energy Research. 2020;**44**(5):3587- 3606. DOI: 10.1002/er.5121

[96] Liu H, Zhu H, Yan L, Huang Y, Kato S, Kojima T. Gasification rate of char with CO2 at elevated temperatures: The effect of heating rate during pyrolysis. Asia-Pacific Journal of Chemical Engineering. 2011;**6**(6):905-911

[97] Wang Q, Zhang R, Luo Z, Fang M, Cen K. Effects of pyrolysis atmosphere and temperature on coal char characteristics and gasification reactivity. Energy Technology. 2016;**4**(4):543-550

[98] Li R, Zhang J, Wang G, Ning X, Wang H, Wang P. Study on CO2 gasification reactivity of biomass char derived from high-temperature rapid pyrolysis. Applied Thermal Engineering. 2017;**121**:1022-1031

[99] Cossu R, Lai T. Automotive shredder residue (ASR) management: An overview. Waste Management. 2015;**45**:143-151

[100] Galvagno S, Fortuna F, Cornacchia G, Casu S, Coppola T, Sharma VK. Pyrolysis process for treatment of automotive shredder residue: Preliminary experimental results. Energy Conversion and Management. 2004;**42**:573-586

[101] Haydary J, Susa D, Gelinger V, Cacho F. Pyrolysis of automobile shredder residue in a laboratory scale screw type reactor. Journal of Environmental Chemical Engineering. 2016;**4**:995-972

[102] Nortanicola M, Cornacchia G, De Gisi S, Di Canio F, Freda C, Garzone P, et al. Pyrolysis of automotive shredder residue in a bench scale rotary kiln. Waste Management. 2017;**65**:92-103

[103] Zolezzi M, Nicolella C, Ferrara S, Iacobucci C, Rovatti M. Conventional and fast pyrolysis of automobile shredder residue (ASR). Waste Management. 2004;**24**:691-699

[104] Chong Z, Yuan S, Ruimeng S. Particle size-dependant properties of a char produced using a moving-bed pyrolyzer for fuelling pulverized coal injection and sintering operations. Fuel Processing Technology. 2019;**190**:1-12

[105] Liu Z, Dugan B, Masiello CA, Gonnermann HM. Biochar particle size, shape, and porosity act together to influence soil water properties. PLoS One. 2017;**12**(6):e0179079

[106] Hameed R, Lei C, Lin D. Adsorption of organic contaminants on biochar colloids: Effects of pyrolysis temperature and particle size. Environmental Science and Pollution Research. 2020;**27**(15):18412-18422

[107] Shar AG, Peng JY, Tian X, Siyal TA, Shar AH, Yuhan J, et al. Contrasting effects of maize residue, coal gas residue and their biochars on nutrient mineralization, enzyme activities and CO2 emissions in sandy loess soil. Saudi Journal of Biological Sciences. 2021;**28**(8):4155-4163

[108] Cunliffe AM, Williams PT. Properties of chars and activated carbons from the pyrolysis of used tyres. Environmental Technology. 1998;**19**(12):1177-1190

[109] Williams PT. Pyrolysis of waste tyres: A review. Waste Management. 2003;**33**(8):1714-1728

[110] Antoniou N, Stavropoulos G, Zabaniotou A. Activation of end of life tyres pyrolytic char for enhancing viability of pyrolysis—Critical review, analysis and recommendations for a hybrid dual system. Renewable & Sustainable Energy Reviews. 2014;**39**:1053-1073. DOI: 10.1016/j. rser.2014.07.143

[111] Makigrianni V, Giannakas A, Bairamis F, Papadaki M, Konstaninou I. Adsorption of Cr(VI) from aqueous solutions by HNO3-purified and chemically activated pyrolytic tire char. Journal of Dispersion Science and Technology. 2017;**38**(7):992-1002

[112] Qu DY, Shi H. Studies of activated carbons used in double-layer capacitator. Journal of Power Sources. 1998;**74**(1):99-107

[113] Joung H-T, Seo Y-C, Kim K-H, Seo Y-C. Effects of oxygen, catalyst and PVC on the formation of PCDDs, PCDFs and dioxin-like PCBs in pyrolysis products of automotive shredder residues. Chemosphere. 2006;**65**(9):1481-1489

[114] Joung H-T, Seo Y-C, Kim K-H. Distribution of dioxins, furans and dioxin-like PCBs in solid products generated by pyrolysis and melting of automotive shredder residue. Chemosphere. 2007;**68**(9):1636-1641

[115] Han Y, Zhao P-P, Dong X-T, Zhang C, Liu S-X. Improvement in electrochemical capacitance of activated carbon from scrap tires by nitric acid treatment. Frontiers in Material Science. 2014;**8**(4):391-398

[116] Shilpa K, Rudra SA. Morphologically tailored activated carbon derived from waste tires as high performance anode for Li-ion battery. Journal of Applied Electrochemistry. 2017;**48**(1):1-13

*PAHs, PCBs and Environmental Contamination in Char Products DOI: http://dx.doi.org/10.5772/intechopen.106424*

[117] Iraola-Arregui I, Van Der Gryp P, Görgens JF. A review on the demineralisation of pre- and postpyrolysis biomass and Tyre wastes. Waste Management. 2018;**79**:667-668

[118] Tang H, Hu H, Li A, Yi B, Li X, Yao D, et al. Removal of impurities of waste pyrolysis char using the molten salt thermal treatment. Fuel. 2021;**301**:121019

[119] Yan LL, Kong L, Qu Z, Lo L, Shen GQ. Magnetic biochar decorated with ZnS nanocrystals for Pb (II) removal. ACS Sustainable Chemistry and Engineering. 2015;**3**:125-132

[120] Yang JP, Zhao YC, Ma SM, Zhu BB, Zhang JY, Zheng CG. Mercury removal by magnetic biochar derived from simultaneous activation and magnetization of sawdust. Environmental Science and Technology. 2016;**50**:12040-12047

[121] Hossain R, Al Mahmood A, Sahajwala V. Recovering renewable carbon materials from automotive shredder residue (ASR) for microsupercapacitor electrodes. Journal of Cleaner Production. 2021;**304**:e127131

[122] Ruffino B, Panepinto D, Zanetti M. A circular approach for the recovery and recycling of automotive shredder residues (ASRs): Material and thermal valorization. Waste and Biomass Valorization. 2021;**12**:3109-3123

[123] Vijayan SK, Kibria MA, Uddin MH, Bhattacharya S. Pretreatment of automotive shredder residues, their chemical characterization and pyrolysis kinetics. Sustainability. 2021;**13**:1-19

[124] Jagodzińska K, Yang W, Jönsson PG, Forsgren C. Can torrefaction be a suitable method of enhancing shredder fines recycling? Waste Management. 2021;**128**:211-220

[125] Zhou Q, Zarei A, De Girolamo A, Yan Y, Zhang L. Catalytic performance of scrap Tyre char for the upgrading of eucalyptus pyrolysis derived bio-oil via cracking and deoxygenation. Journal of Analytical and Applied Pyrolysis. 2019;**139**:167-176

[126] Li N, Li Y, Ban Y, Song Y, Zhi K, Teng Y, et al. Direct production of high hydrogen syngas by steam gasification of Shengli lignite/chars: Remarkable promotion effect of inherent minerals and pyrolysis temperature. International Journal of Hydrogen Energy. 2017;**42**(9):5865-5872

[127] Bai Y, Lv P, Li F, Song X, Su W, Yu G. Investigation into Ca/Na compounds catalysed coal pyrolysis and char gasification with steam. Energy Conversion and Management. 2019;**184**:172-179

[128] Luo S, Xiao B, Hu Z, Liu S, Guo X, He M. Hydrogen-rich gas from catalytic steam gasification of biomass in a fixed bed reactor: Influence of temperature and steam on gasification performance. International Journal of Hydrogen Energy. 2009;**34**(5):2191-2194

[129] Xu MX, Wu YC, Nan DH, Lu Q, Yang YP. Effects of gaseous agents on the evolution of char physical and chemical structures during biomass gasification. Bioresource Technology. 2019;**292**:121994-121994

[130] Loftfian S, Ahmed H, El-Geassy A-HA, Samuelsson C. Alternative reducing agents in metallurgical processes: Gasification of shredder residue material. Journal of Sustainable Metallurgy. 2016;**3**(2):336-349

[131] Wang L, Chen L, Tsang DCW, Kua HW, Yang J, Ok YS, et al. The roles of biochar as a green admixture for sediment-based construction products. Cement and Concrete Composites. 2019;**104**:103348

[132] Caetano JA, Schalch V, Pablos JM. Characterization and recycling of the fine fraction of automotive shredder residue (ASR) for concrete paving blocks production. Clean Technologies and Environmental Policy. 2020;**22**:835-847

[133] Azara A, Belbessai S, Abatzoglou N. A review of filamentous carbon nanomaterial synthesis via catalytic conversion of waste plastic pyrolysis products. Journal of Environmental Chemical Engineering. 2022;**10**(1):107049. DOI: 10.1016/j. jece.2021.107049

[134] Williams PT. Hydrogen and carbon nanotubes from pyrolysis-catalysis of waste plastics: A review. Waste and Biomass Valorization. 2020;**12**(1):1-28. DOI: 10.1007/s12649-020-01054-w

[135] European Commission. 2019. Regulation (EU) 2019/1021 of the European Parliament and of the Council of 20 June 2019 on persistent organic pollutants. Available from: EUR-Lex-32019R1021-EN-EUR-Lex(europa.eu)

[136] Elkhalifa S, Al-Ansari T, Mackey HR, McKay G. Food waste to biochars through pyrolysis. Resources, Conservation and Recycling. 2019;**144**:310-320. DOI: 10.1016/j. resconrec.2019.01.024

[137] Bolognesi S, Bernardi G, Callegari A, Dondi D, Capodaglio AG. Biochar production from sewage sludge and microalgae mixtures: Properties, sustainability and possible role in circular economy. Biomass Conversion and Biorefinery. 2019;**11**(2):289-299. DOI: 10.1007/s13399-019-00572-5

[138] Porshnov D. Evolution of pyrolysis and gasification as waste to energy

tools for low carbon economy. Wiley Interdisciplinary Reviews. Energy and Environment. 2022;**11**(1):e421–n/a. DOI: 10.1002/wene.421

[139] UK Government. 2022. Landfill Tax rates 2022/23. Landfill Tax rates for 2022 to 2023- GOV.UK. Available from: www. gov.uk

### *Edited by Mattia Bartoli, Mauro Giorcelli and Alberto Tagliaferro*

Biochar is the carbonaceous residue produced from the pyrolytic conversion of biomass. It is generally used for agricultural applications as a soil amendment but has far wider potential. This book presents the use of biochar as a platform for the development of new intriguing solutions in several cutting-edge fields. The book is a useful reference volume for any reader with a strong scientific and technological background, ranging from scientific advisors in private companies to academic researchers promoting the spread of knowledge about biochar to anyone not already working with it.

Published in London, UK © 2023 IntechOpen © taviphoto / iStock

Biochar - Productive Technologies, Properties and Applications

Biochar

Productive Technologies, Properties

and Applications

*Edited by Mattia Bartoli,* 

*Mauro Giorcelli and Alberto Tagliaferro*