**3. Applications to chemical and process engineering**

In recent decades, there have been a large number of studies using ANNs in chemical engineering, from molecular property prediction [12], fault diagnosis [13], predictive control [14], and optimization [15, 16]. The use of first-principles knowledge must be integrated with the neural network in order to retain a more physical understanding of the system [14]. In the following subsections, we presented the principal papers of each area, with tables summarizing the characteristics of the ANNs used.

### **3.1 Thermodynamics and transport phenomena**

Several data-driven models have been employed to predict phase equilibrium and transport phenomena coefficients for various chemical systems [17]. Indeed, these fields already have some empiricism in their standard mathematical formulations. For example, flash algorithms have some empiricism when using binary interaction parameters in subjective mixing rules [18], and the majority of transport phenomena coefficients are estimated from empirical correlations, sometimes questionable [19]. Therefore, the use of ANNs is a better way to find functional relationships between the model variables instead of first determining these constants [20].

Moreover, ANNs reveal a conceivably faster choice to those property prediction calculations in process simulations, limiting process control applications that require to be conducted in real-time. For this, Poort et al. [21] studied the replacement of conventional Equations of State (EoS) for property and phase stability calculations on a binary mixture of methanol–water. They trained ANNs with data generated through the Thermodynamics for Engineering Applications (TEA) to represent four kinds of flash algorithms, leading to an enhancement of 15 times for the predictions of properties and 35 times for classification of the phases.

Also noteworthy is that ANNs have also been used to predict if a particular mixture forms an azeotrope – essential information to design and to control a separation process. Alves et al. [22] successfully developed an ANN classification model to determine whether binary mixtures can exhibit (or not) azeotropy based solely on the properties of pure components as input variables. Therefore, it shows the power of ANNs for this type of thermodynamic evaluation since it does not take into account the non-ideality of the mixture.

*Deep Learning Applications*

in this subject [3].

nent for the operation of an industrial process.

responsible for the generation of the data [6].

data for predicting the corresponding output data.

**engineering applications**

dropout rate, etc.).

historical data, obtained through sensors that measure thousands of variables in the order of seconds [3]. The analysis and exploitation of these data is a critical compo-

Modeling, simulation, and optimization are essential activities and competitive differentials among researchers to meet the challenges produced by environmental and commercial restrictions. In this context, ANNs are mostly used in process prediction and classification, as they are a robust nonlinear regression. In particular, this technique should be used when the solution of a problem is hampered by some of the following points: lack of physical or statistical understanding of the problem, statistical variations of the observable data, and the nonlinear mechanism

In general terms, the use of neural networks consists of the following steps: 1- establishing the network architecture; 2- providing experimental data; 3- adjusting the network parameters – also known as their weights – until they learn the phenomenon (step called training); and 4- using the trained network with new input

ANNs have been successfully applied to chemistry to correlate spectra of analytical methods and product properties [7]; in catalysis, to determine the relationships between the catalyst structure and its activity [8]; in process modeling, to predict product performance and operating conditions [9], and particularly in process control and fault diagnosis [10]. The main reasons for the growing popularity of the neural network approach are its lower computational cost compared to other methods and its ability to solve complex nonlinear problems [5]. Therefore, this review study demonstrates the increasing use of ANNs in Chemical and Process Engineering, helping to understand and to explore process data aspects for future research.

**2. Most common activation functions used in chemical and process** 

A neural network contains hyperparameters to be tuned prior to training in order to achieve the best configuration. Among them, the following can be mentioned: (i) number of hidden neurons, (ii) activation function, (iii) optimizer, and (iv) regularization and their dependencies (learning rate, optimizer specific,

Particularly, activation functions determine the output of the model, its accuracy, and the computational efficiency of training a model; therefore, they are an essential part of the structure of the neural networks. The Sigmoid function, Hyperbolic Tangent (TanH), and ReLU (Rectified Linear Unit) are the most

In this framework, the so-called Artificial Neural Networks (ANNs) have numerous advantages and applications. They are universal nonlinear approximators based on the human brain-behavior through interconnected neurons that learn tasks from experience; in that case, from data [4]. Similarly to the nervous system, artificial neural networks are organized in the form of several simple individual elements – nodes or neurons – which interconnect with each other, forming networks capable of storing and transmitting information from/to the outside. Another relevant capacity of ANNs is their plasticity, which, through a learning process, allows changing in the interconnection pattern of its elements [5]. ANNs have been widely used in modeling or regression (linear and nonlinear) for one or several independent variables. It is worth noting that their use is not new in Chemical and Process Engineering, dating from the 1980s with some progress along the way, decisively contributing to the resurgence of the interest of the scientific community

**168**

They are also widely employed to predict thermal-physical properties of ionic liquids, such as density and viscosity [23]. The primary source of these values comes from experiments at the laboratory since ionic liquids do not present a universal description of their phase behavior. For example, using the definition of group contribution and the operating temperature, Valderrama et al. [24] successfully developed a three-layer FF-ANN to estimate the density of ionic liquids.

ANNs have also been employed in statistical thermodynamics techniques, which compute physicochemical properties from molecular simulations. One of these methods – the High-Throughput Force Field Simulation (HT-FFS) – can generate large volumes of data. ANNs can be trained with these data, thus building a graybox model to improve the property predictions with a lower computational effort [25]. They have also been used in Density Functional Theory (DFT) calculations to replace some physical functionals with data-driven ones, finding the energy levels for electronic structures of different compounds with a balance between computational cost and accuracy [26].

Regarding their application to Transport Phenomena, it is well-known that ANNs – as an excellent universal approximator for any nonlinear function [27] – can be used for estimating convective heat- and mass-transfer coefficients [17]. Mainly in situations in which there is no mathematical correlation that can adjust them, as is the case of bubble columns. For this, Verma and Srivastava [19] successfully built an ANN model from literature data with eight inputs related to the system configuration of a bubble column (gas velocity, Prandtl number, number of holes, hole diameter, column diameter, surface tension, gas holdup, and bed height) and one output (heat coefficient).

**Table 1** displays a summary of the current applications of neural networks to thermodynamics and transport phenomena discussed above. In the table, we specify the field, case study, class of neural network, activation function, topology and software used in each work.

### **3.2 Kinetics and catalysis**

Neural networks have been successfully applied to catalysis to determine the relationship between the catalyst structure and its activity [8]. As heterogeneous catalysis has developed increasingly efficient experimentation techniques, the number of new data have increased exponentially [28], both from synthesis and from characterization and catalytic tests [29]. Thus, there is a need for more adequate tools to manage these large amounts of experimental data, to understand and to model it, and to generate a way to optimize the catalytic performance [30].

Two types of ANNs applications have been described so far in the frame of combinatorial catalysis: (i) ANN catalyst compositional models, correlating composition and synthesis variables with catalytic performance, and (ii) ANN kinetic models, correlating reaction conditions with the catalytic performance [31]. For example, those applications include the design of ammoxidation of propylene catalyst [32], design of methane oxidative decoupling catalyst [33], analysis and prediction of results of the decomposition of NO over zeolites [34], among other studies. Also, ANNs have been used combined with genetic algorithms for designing propane ammoxidation catalysts [35]. Another work successfully reported the viability of ANNs in the analysis and prediction of catalytic results within a collection of catalysts produced by combinatorial techniques [36]. Recently, an ANN was applied to estimate the rate of dehydration reaction of methanol in dimethyl ether synthesis [37]. The results showed that an ANN is a powerful tool for evaluating the reaction rate instead of using sophisticated kinetic model equations.

**171**

*\**

**Table 1.**

ANNs [38].

*Application of Artificial Neural Networks to Chemical and Process Engineering*

equilibrium of NH3/H2O and CH4/C2H6 systems

Determination of reduced boiling point from molecular weight and acentric factor

> flash calculations

of azeotrope formation

of physical properties of ionic liquids

Enhancing the High-Throughput Force Field Simulation (HT-FFS)

Correlation functionals of the electronic density

**Neural Network**

FF-ANN Linear/

FF-ANN Linear/

Fully connected neural networks

Sigmoid

ELU\*\*

**Activation Function**

FF-ANN\* Sigmoid 2–13-2 in-house

FF-ANN Sigmoid 2–2–2-1 Matlab

FF-ANN Sigmoid 16–6-1 in-house

FF-ANN Tanh 10–15–15-1 Matlab

Sigmoid 4–8 neurons

in each hidden layer

**Topology\*\*\* Software**

3–10-2 Keras-

25–16–8-4-3 PyTorch

software

Python

software

TensorFlow

The number of publications in this catalysis field has had an upward trend, especially in the last decade with the high demand for practical applications of the concepts of Big Data. The group of Turkish researchers led by Günay and Yildirim has excelled with work in the field, using not only ANNs for extracting knowledge from catalytic data, but also decision tree algorithms to determine the heuristic conditions and rules that lead to a high performance of the catalyst. For example, in work about carbon monoxide oxidation over Cu-based catalysts, they successfully used 1337 data points from 20 studies for evaluating catalyst performance using

*\*\*\*The first and last elements in topology represent the number of neurons in the input and in the output layer,* 

In the field of heterogeneous catalysis, ANNs can be used to select better possible catalysts – cheaper, less toxic, and composed of non-precious metals – for a given reaction, thus reducing the massive number of needed high-throughput experiments, peculiar conjuncture of combinatorial catalysis [39]. In this direction, Cavalcanti et al. [40] used a three-layer feedforward neural network to predict the

*DOI: http://dx.doi.org/10.5772/intechopen.96641*

[18] Phase Equilibrium Vapor–Liquid

Phenomena

[21] Phase Equilibrium Vapor–liquid

[22] Phase Equilibrium Prediction

[24] Ionic Liquids Estimation

Thermodynamics

Thermodynamics

*FN-ANN stands for Feed-Forward Artificial Neural Network.*

*respectively. Among them, the number of neurons in the hidden layer(s).*

*Current applications of ANNs to thermodynamics and transport phenomena.*

[20] Transport

[25] Molecular

[26] Molecular

*\*\*ELU stands for Exponential Linear Unit.*

**References Field Case Study Class of** 


*Application of Artificial Neural Networks to Chemical and Process Engineering DOI: http://dx.doi.org/10.5772/intechopen.96641*

*\* FN-ANN stands for Feed-Forward Artificial Neural Network.*

*\*\*ELU stands for Exponential Linear Unit.*

*\*\*\*The first and last elements in topology represent the number of neurons in the input and in the output layer, respectively. Among them, the number of neurons in the hidden layer(s).*

#### **Table 1.**

*Deep Learning Applications*

tional cost and accuracy [26].

and one output (heat coefficient).

and software used in each work.

**3.2 Kinetics and catalysis**

They are also widely employed to predict thermal-physical properties of ionic liquids, such as density and viscosity [23]. The primary source of these values comes from experiments at the laboratory since ionic liquids do not present a universal description of their phase behavior. For example, using the definition of group contribution and the operating temperature, Valderrama et al. [24] successfully

ANNs have also been employed in statistical thermodynamics techniques, which

compute physicochemical properties from molecular simulations. One of these methods – the High-Throughput Force Field Simulation (HT-FFS) – can generate large volumes of data. ANNs can be trained with these data, thus building a graybox model to improve the property predictions with a lower computational effort [25]. They have also been used in Density Functional Theory (DFT) calculations to replace some physical functionals with data-driven ones, finding the energy levels for electronic structures of different compounds with a balance between computa-

Regarding their application to Transport Phenomena, it is well-known that ANNs – as an excellent universal approximator for any nonlinear function [27] – can be used for estimating convective heat- and mass-transfer coefficients [17]. Mainly in situations in which there is no mathematical correlation that can adjust them, as is the case of bubble columns. For this, Verma and Srivastava [19] successfully built an ANN model from literature data with eight inputs related to the system configuration of a bubble column (gas velocity, Prandtl number, number of holes, hole diameter, column diameter, surface tension, gas holdup, and bed height)

**Table 1** displays a summary of the current applications of neural networks to thermodynamics and transport phenomena discussed above. In the table, we specify the field, case study, class of neural network, activation function, topology

Neural networks have been successfully applied to catalysis to determine the relationship between the catalyst structure and its activity [8]. As heterogeneous catalysis has developed increasingly efficient experimentation techniques, the number of new data have increased exponentially [28], both from synthesis and from characterization and catalytic tests [29]. Thus, there is a need for more adequate tools to manage these large amounts of experimental data, to understand and to model it, and to generate a way to optimize the catalytic performance [30].

Two types of ANNs applications have been described so far in the frame of combinatorial catalysis: (i) ANN catalyst compositional models, correlating composition and synthesis variables with catalytic performance, and (ii) ANN kinetic models, correlating reaction conditions with the catalytic performance [31]. For example, those applications include the design of ammoxidation of propylene catalyst [32], design of methane oxidative decoupling catalyst [33], analysis and prediction of results of the decomposition of NO over zeolites [34], among other studies. Also, ANNs have been used combined with genetic algorithms for designing propane ammoxidation catalysts [35]. Another work successfully reported the viability of ANNs in the analysis and prediction of catalytic results within a collection of catalysts produced by combinatorial techniques [36]. Recently, an ANN was applied to estimate the rate of dehydration reaction of methanol in dimethyl ether synthesis [37]. The results showed that an ANN is a powerful tool for evaluating the reaction rate instead of using sophisticated kinetic model

developed a three-layer FF-ANN to estimate the density of ionic liquids.

**170**

equations.

*Current applications of ANNs to thermodynamics and transport phenomena.*

The number of publications in this catalysis field has had an upward trend, especially in the last decade with the high demand for practical applications of the concepts of Big Data. The group of Turkish researchers led by Günay and Yildirim has excelled with work in the field, using not only ANNs for extracting knowledge from catalytic data, but also decision tree algorithms to determine the heuristic conditions and rules that lead to a high performance of the catalyst. For example, in work about carbon monoxide oxidation over Cu-based catalysts, they successfully used 1337 data points from 20 studies for evaluating catalyst performance using ANNs [38].

In the field of heterogeneous catalysis, ANNs can be used to select better possible catalysts – cheaper, less toxic, and composed of non-precious metals – for a given reaction, thus reducing the massive number of needed high-throughput experiments, peculiar conjuncture of combinatorial catalysis [39]. In this direction, Cavalcanti et al. [40] used a three-layer feedforward neural network to predict the

ideal composition of the catalyst in the water-gas-shift reaction and discover useful trends through sensitivity analysis. The input variables for ANN were several, while the only output variable considered was the conversion of CO. The model for the reaction was successfully developed, exhibiting the power of ANNs for predicting better catalysts and operating conditions for the process.

Recently, Cavalcanti et al. [8] showed that ANNs are able to predict the variables that most influence the conversion of CO in the water-gas-shift reaction, that is, temperature and surface area. The results can be used to conduct subsequent research in an optimized manner in this area, as it aims at the well-managed use of environmental resources, in the sense of selecting efficient catalysts for producing hydrogen - a clean energy source.

In the same topic, Garona et al. [41] presented an empiric model for the Fischer-Tropsch Synthesis (FTS) reaction using ANNs. A database of FTS to light olefins was assembled from the literature, and feedforward neural networks were used to build more complete models, which helped to predict optimal catalyst composition and operating conditions.

It is also noteworthy that ANNs were also used to model the sintering of a catalyst in a dry reformer [42]. In particular, the effects of temperature, pressure, and catalyst diameter on methane and CO2 conversions, H2/CO ratio, and molar percentage of solid carbon deposited on the catalyst (responsible for deactivation) have been studied. The ANN design activity was automated using a Genetic Algorithm (GA) search over the set of possible network topologies. The inclusion of the effective number of parameters in the GA objective function led to networks that performed well over testing data points.

Another application is in the determination of acidity in zeolites with data from FTIR spectroscopy [43]. FF-ANNs were used for analyzing multivariate base on the characteristic absorbance of 11 zeolite samples after metal substitution (Zn, Cu, Ga, and Ag) in the ~3612 cm−1 region. The developed regression method presented the same results of acid sites from other conventional and expensive methodologies.

Thus, in order to formulate a new kind of catalyst, it is essential to identify the catalysis past [44]. Therefore, by using ANNs, it is possible to convert historical data from past publications into valuable information, leading to a great acceleration in the development of new catalysts with better performances for a given process [8]. **Table 2** presents a summary of the current applications of neural networks to catalytic processes.
