2. A PCA in a switchgrass composition

Biofuels are a propitious alternative to the fossil fuels reliance, due to their sustainable production based on using biomass as a raw material. Biomass is an organic matter derived from living or recently living beings. The production of biofuels also has some advantages from those produced from non-sustainable resources, for instance, the reduction of greenhouse emissions (GHG) such as the CO2. Another benefit of using biofuels is the development of an agricultural industry and the creation of rural jobs to produce and deliver biomass to biorefineries. Bioethanol is one type of biofuel that can be produced from organic resources such as corn stover, miscanthus, and switchgrass, among others. Bioethanol has several applications in a wide variety of industries. According to government dependencies such as the Energy Information Administration (EIA), an increment of biofuels is expected in the U.S. within the coming years as product of the Renewable Fuel Standard [1] that requires more production of fuel

Biofuels are classified according to the raw material utilized to produce them: (1) the firstgeneration is related to biofuels produced from edible biomass that can be generally used for human consumption (e.g., corn, sugarcane, sugar beet, among others), (2) the secondgeneration are biofuels generated from a wide range of feedstock, including lignocellulosic biomass (LCB), such as perennial grasses, soft and hard wood, up to municipal solid waste, and (3) the third-generation commonly refers to biofuels produced from algal biomass [2]. Biofuels produced from LCB are a feasible option in the U.S. for the coming years as they utilize non-food feedstock and can be grown in marginal lands or are byproducts produced

Biofuel production requires improvements in strategic areas such as conversion technologies, genetic manipulation of feedstock breeds, and supply chain management of biomass from harvesting areas to conversion facilities, among others in order to become a plausible alternative to fossil fuel production. Supply chain (SC) improvement represents an important area of opportunity in biofuel production due to the fact that environmental, geographical and economic factors are related to operations like harvesting, handling, storing as well as transportation, which have shown significant impact in biofuel yields/cost. Studies have demonstrated that some factors such as storage time affects the physical and chemical properties of the biomass [3], therefore, biomass properties have an important role in the design of the opera-

There are many properties that affect the conversion efficiency depending on the type of conversion technology that is being utilized for the production of biofuels. Moisture is a property of biomass that affects both the thermochemical and biochemical conversion technologies and a drying process that diminishes the moisture content in the biomass up to the required level of humidity is needed. The drying process to meet the specification for the selected conversion technology incurs in a cost that could be reduced/controlled with the implementation of logistics processes and infrastructure design that consider the level of humidity in the feedstock. The utilization of the biomass without meeting the expected specifications could lead the production of biofuels to an inefficient conversion process. Another example of the importance of the biomass properties are the carbohydrates. If the level of carbohydrates does not meet the specification, then, the amount of biofuel derived from that

utilizing renewable resources (e.g., 16 billion gallons for 2022).

tions required for the production and distribution of biofuel [4].

from the wood industry.

40 Advances in Biofuels and Bioenergy

This analysis focuses on LCB [specifically, on switchgrass (Panicum virgatum)], which is a feedstock derived from organic matter that is mainly composed of cellulose and hemicellulose. Examples of LCB are corn stover, wheat straw and switchgrass and there are many activities involved in the production and distribution of this kind of biomass such as harvesting, extracting, packaging, transporting, handling, among others. There are factors in the aforementioned activities that affect the physical and chemical properties of the biomass, and thus, their qualityrelated costs. The consideration of those factors has an effect in the design and implementation of the supply chain (SC).

An example of the relationship between the biomass properties and the SC design is the cellulose and hemicellulose (carbohydrates) contents in the LCB. The cellulose and hemicellulose content in the LCB is directly related to the biofuel produced in the conversion process since the carbohydrates are the main component to produce the energy. The more carbohydrates contained in the biomass, the more liters of biofuel obtained from that particular batch of feedstock. Hence, activities that affect the carbohydrate contents need to be improved to minimize the impact on the conversion process.

Densification is one of the processes in the SC of biofuel production that affects some of the properties within the biomass. The densification of biomass consists in conglomerating the organic matter in the form of compact structures such as briquettes and pellets, to improve their handling, storage and transportation but also to reduce the level of dry matter loss (DML) in the feedstock. The DML is directly related with the loss of carbohydrates in the biomass. The less organic matter in the batch, the less amount of cellulose and hemicellulose to produce biofuels. Densification also affects other properties in the biomass such as the moisture content, unit density, durability index, as well as other properties specified for the conversion process according to the implemented technology. Controlling the physical and chemical properties under the specification requirements is vital for an efficient conversion. Delivering biomass that does not meet the specifications could lead to extra costs as a consequence of re-processing the biomass up to meeting the specifications.

The reduction of the variance within the physical and chemical properties of the biomass helps to avoid extra operational cost due to re-processing of feedstock that is out of the specifications. Identifying the factors, which are involved in production and distribution activities that affect the physical/chemical properties, is necessary to design an efficient SC. Researchers in the field have studied baling effects in feedstock properties [5–7]. In previous works, Aboytes-Ojeda et al. [3] proposes a PCA to detect those factors that have a significant impact on properties of interest and that should be approached by implementing novel operations and strategies in order to fulfill the conversion specifications.

Table 1 identifies the factors and variables included in the analysis. The storage days were classified in three groups or levels: 75, 150 and 225 days of storage. The particle size was defined in three groups or levels: PS1 (243.84 cm), PS2 (7.62 cm) and PS3 (1.27–1.91 cm). The wrap type was categorized in two levels: (i) net mesh and net (excluding the two ending parts of the bale), and (ii) the high tensile strength film wrapping for the complete bale (net and film). The bale weight has two levels for this study; the lower level was for bale with a weight between 957.65 and 1715.20 lb., whereas the high level was for bale with a weight between 1715.21 and 2455.10 lb. The weight in the bale has repercussions in logistic operations such as

Particle size 3 Cellulose, hemicellulose, lignin, ash and extractives Wrap type 2 Cellulose, hemicellulose, lignin, ash and extractives Storage days 3 Cellulose, hemicellulose, lignin, ash and extractives Bale weight 2 Cellulose, hemicellulose, lignin, ash and extractives

Modeling and Optimization of Quality Variability for Decision Support Systems in Biofuel Production

http://dx.doi.org/10.5772/intechopen.73111

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Factors No. groups/levels Variables

Five variables were included in the analysis. The cellulose is a glucose polymer linked by glycosidic bonds and the hemicellulose is a branched polymer of carbon sugars. The lignin refers to a structural component of plants, consisting of an aromatic system made of phenyl proposal units. The ash is considered as the inorganic leftover after the combustion process at 550–600C. The extractives are non-structural components that can include free sugars, proteins, chlorophyll, and waxes. The PCA methodology proposed uses the variability within the variables (i.e., variance and covariance) to create artificial variables and then it groups the data according to their corresponding factor group/level. Finally, a statistical comparison of means is utilized to conclude if there is a significant difference between the means of every factor group/level. Figure 1 shows the methodology to perform the PCA which consists of five basic

In order to implement the PCA [9, 10], it is necessary to test the required data conditions to perform the analysis, then, the covariance/correlation matrix needs to be calculated. A Bartlett's test of sphericity is utilized next to determine if the correlation information for the analysis is significant. With the covariance/correlation matrix, eigenvalues and eigenvectors are computed. The eigenvalue is utilized to determine the portion of variance attributed to the corresponding eigenvector. The components of the eigenvectors known as loadings are used to transform the original data into the components scores. The variance matrix is shown in Table 2; there is no need to compute the correlation matrix since all the measures for every variable are in the same scale. The portion of variance in PCA is calculated according to the eigenvalues obtained in the analysis. The idea behind the PCA is to detect those components with the higher eigenvalues;

handling, storage, and transportation.

Table 1. Factors and variables for PCA.

2.2. Principal component analysis (PCA)

steps.

The multivariate methodology proposed by Aboytes-Ojeda et al. [3] intends to: (1) introduce the covariance information analysis to draw systematic insights about the factors under study, and (2) present a novel methodology to identify the contribution of every factor in the analysis with respect to the total system variability. The variables introduced in the analysis were cellulose, hemicellulose, lignin, ash and extractives content; whereas the factors were the particle size in the bale, the wrap material, the days in storage and the weight of the bale.
