In this study, we applied the concept of reinforcement learning to surgical procedures and identified common points between reinforcement learning and the way surgeons approach an operation. Reinforcement learning is now exclusively used and studied in the context of game play; however, it could also be applied to performing surgeries now that robotic surgery is widely available. Although there are many hurdles to overcome, AI could be applied to surgery by setting the appropriate rules, such as defining rewards and playable (operable) areas. To realize this goal, it is important for clinicians to further study deep learning and

We greatly appreciate American Journal Experts for their generous help in editing the

https://www.intechopen.com/download/index/process/160/authkey/f5524cabe6c92eff86119

clinicians and engineers should work cooperatively define rules.

reinforcement learning strategies.

**Acknowledgements**

**Conflict of interest**

**Supporting information**

Authors declare there is no conflict of interests.

**Movie 1.** Result of Game 1 after 10 epochs of learning. **Movie 2.** Result of Game 1 after 500 epochs of learning. **Movie 3.** Result of Game 1 after 1500 epochs of learning. **Movie 4.** Result of Game 2 after 500 epochs of learning. **Movie 5.** Result of Game 2 after 1500 epochs of learning. **Movie 6.** Result of Game 3 after 1500 epochs of learning.

manuscript.

b2f0c58ba26

**Figure 2.** Choice of the shortest route. Blue and red arrows in the circle represent the shortest routes. Only the route of the blue arrows was chosen after several trials.

In reinforcement learning, it is preferable to obtain a reward during the immediately following action rather than having expectations to obtain rewards in distant future actions [6]. This can be understood by the results of Task 1, in which the knife passed all the green areas in the shortest possible time, even though a rule never inhibited a more roundabout approach (**Figure 1B**). This can be expected to as a way to shorten the surgical time. Furthermore, choosing the shortest route was also of interest. The blue and red arrows in the circle in **Figure 2** represent the shortest routes; the route of the blue arrows was chosen after several trials (**Figure 1B**). The method used to decide the next action in reinforcement learning is maximizing the expected values that would be obtained after subsequent actions [6]. Therefore, actions that have greater expected values are likely to be preferred. Therefore, we expected that the red arrows were not preferred because points would be deducted (red area) or the task would be ended (uterus) with subsequent actions. This approach is also considered superior in surgery. In other words, avoiding areas associated with point deductions would result in retaining the appropriate margins during surgery. Therefore, the characteristics of reinforcement learning seemed to be compatible with typical surgical approaches.

The other results obtained were expected. It was concluded that reinforcement learning could solve a simple 2D task; 3D models that more closely replicate surgery should be further considered. In addition, increasing the playable area provides significantly more options for actions, and high-performance computers and tuning of neural networks will be needed for more complex tasks. 3D operation models are currently being developed and are used in practice for endoscopic surgery simulation [11–14]. The speed at which high-performance computers are being developed is astonishing, and deep learning is being thoroughly investigated by engineers both in academia and business [15–17]. Combining the progress in both fields may provide designs allowing AI to realistically perform surgery. In such areas, clinicians can contribute the essential aspects of the surgery, that is, the playable task areas and the appropriate scores. Although increasing reward areas or limiting playable areas would speed learning time, such restrictions could also prevent the AI from finding alternative paths. Thus, clinicians and engineers should work cooperatively define rules.

In this study, we applied the concept of reinforcement learning to surgical procedures and identified common points between reinforcement learning and the way surgeons approach an operation. Reinforcement learning is now exclusively used and studied in the context of game play; however, it could also be applied to performing surgeries now that robotic surgery is widely available. Although there are many hurdles to overcome, AI could be applied to surgery by setting the appropriate rules, such as defining rewards and playable (operable) areas. To realize this goal, it is important for clinicians to further study deep learning and reinforcement learning strategies.

## **Acknowledgements**

We greatly appreciate American Journal Experts for their generous help in editing the manuscript.

## **Conflict of interest**

In reinforcement learning, it is preferable to obtain a reward during the immediately following action rather than having expectations to obtain rewards in distant future actions [6]. This can be understood by the results of Task 1, in which the knife passed all the green areas in the shortest possible time, even though a rule never inhibited a more roundabout approach (**Figure 1B**). This can be expected to as a way to shorten the surgical time. Furthermore, choosing the shortest route was also of interest. The blue and red arrows in the circle in **Figure 2** represent the shortest routes; the route of the blue arrows was chosen after several trials (**Figure 1B**). The method used to decide the next action in reinforcement learning is maximizing the expected values that would be obtained after subsequent actions [6]. Therefore, actions that have greater expected values are likely to be preferred. Therefore, we expected that the red arrows were not preferred because points would be deducted (red area) or the task would be ended (uterus) with subsequent actions. This approach is also considered superior in surgery. In other words, avoiding areas associated with point deductions would result in retaining the appropriate margins during surgery. Therefore, the characteristics of reinforcement learning seemed to be compatible with typi-

**Figure 2.** Choice of the shortest route. Blue and red arrows in the circle represent the shortest routes. Only the route of

The other results obtained were expected. It was concluded that reinforcement learning could solve a simple 2D task; 3D models that more closely replicate surgery should be further considered. In addition, increasing the playable area provides significantly more options for actions, and high-performance computers and tuning of neural networks will be needed for more complex tasks. 3D operation models are currently being developed and are used in

cal surgical approaches.

the blue arrows was chosen after several trials.

358 Artificial Intelligence - Emerging Trends and Applications

Authors declare there is no conflict of interests.

## **Supporting information**

**Movie 1.** Result of Game 1 after 10 epochs of learning.

**Movie 2.** Result of Game 1 after 500 epochs of learning.

**Movie 3.** Result of Game 1 after 1500 epochs of learning.

**Movie 4.** Result of Game 2 after 500 epochs of learning.

**Movie 5.** Result of Game 2 after 1500 epochs of learning.

**Movie 6.** Result of Game 3 after 1500 epochs of learning.

https://www.intechopen.com/download/index/process/160/authkey/f5524cabe6c92eff86119 b2f0c58ba26

**S7 Text.** The game codes for Game 1, 2 and 3 (python script). The code of agent.py, memory. py and game.py could be obtained in the link in Section 2.

http://dx.doi.org/10.5772/intechopen.76146

**S7 Text.** The game codes for Game 1, 2 and 3 (python script). The code of agent.py, memory.

py and game.py could be obtained in the link in Section 2.

[5] Gibney E. Google AI algorithm masters ancient game of Go. Nature. 2016;**529**(7587):

Can Reinforcement Learning Be Applied to Surgery? http://dx.doi.org/10.5772/intechopen.76146 381

[6] Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, et al. Humanlevel control through deep reinforcement learning. Nature. 2015;**518**(7540):529-533. DOI:

[7] Littman ML. Reinforcement learning improves behaviour from evaluative feedback. Nature. 2015;**521**(7553):445-451. DOI: 10.1038/nature14540. PubMed PMID: 26017443 [8] Wallace SK, Fazzari MJ, Chen H, Cliby WA, Chalas E. Outcomes and postoperative complications after hysterectomies performed for Benign compared with malignant indications. Obstetrics and Gynecology. 2016;**128**(3):467-475. DOI: 10.1097/AOG.0000000000001591.

[9] Rampasek L, Goldenberg A. TensorFlow: Biology's gateway to deep learning? Cell Systems. 2016;**2**(1):12-14. DOI: 10.1016/j.cels.2016.01.009. PubMed PMID: 27136685 [10] Schmidhuber J. Deep learning in neural networks: An overview. Neural Networks.

[11] Beyer-Berjot L, Berdah S, Hashimoto DA, Darzi A, Aggarwal R. A virtual reality training curriculum for laparoscopic colorectal surgery. Journal of Surgical Education. 2016;**73**(6):932-941. DOI: 10.1016/j.jsurg.2016.05.012. Epub 2016/06/28; PubMed PMID:

[12] Khan ZA, Kamal N, Hameed A, Mahmood A, Zainab R, Sadia B, et al. SmartSIM—A virtual reality simulator for laparoscopy training using a generic physics engine. The International Journal of Medical Robotics + Computer Assisted Surgery: MRCAS.

2016;**16**:437. DOI: 10.1002/rcs.1771. Epub 2016/09/28; PubMed PMID: 27671920 [13] Li XL, Du DF, Jiang H. The learning curves of robotic and three-dimensional laparoscopic surgery in cervical cancer. Journal of Cancer. 2016;**7**(15):2304-2308. DOI: 10.7150/ jca.16653. PubMed PMID: 27994668; PubMed Central PMCID: PMCPMC5166541 [14] Romero-Loera S, Cárdenas-Lailson LE, de la Concha-Bermejillo F, Crisanto-Campos BA, Valenzuela-Salazar C, Moreno-Portillo M. Skills comparison using a 2D vs. 3D laparoscopic simulator. Cirugia y Cirujanos. 2016;**84**(1):37-44. DOI: 10.1016/j.circen.2015.12.012.

[15] Kusy M, Zajdel R. Application of reinforcement learning algorithms for the adaptive computation of the smoothing parameter for probabilistic neural network. IEEE Transactions on Neural Networks and Learning Systems. 2015;**26**(9):2163-2175. DOI:

[16] Senda K, Hattori S, Hishinuma T, Kohda T.Acceleration of reinforcement learning by policy evaluation using nonstationary iterative method. IEEE Transactions on Cybernetics. 2014;**44**(12):2696-2705. DOI: 10.1109/TCYB.2014.2313655. PubMed PMID: 24733037 [17] Xu B, Yang C, Shi Z. Reinforcement learning output feedback NN control using deterministic learning technique. IEEE Transactions on Neural Networks and Learning Systems.

2014;**25**(3):635-641. DOI: 10.1109/TNNLS.2013.2292704. PubMed PMID: 24807456

10.1109/TNNLS.2014.2376703. PubMed PMID: 25532211

2015;**61**:85-117. DOI: 10.1016/j.neunet.2014.09.003. PubMed PMID: 25462637

445-446. DOI: 10.1038/529445a. Epub 2016/01/29, PubMed PMID: 26819021

10.1038/nature14236. PubMed PMID: 25719670

PubMed PMID: 27500339

27342755

(English Edition)

## **Author details**

Masakazu Sato\*, Kaori Koga, Tomoyuki Fujii and Yutaka Osuga

\*Address all correspondence to<EMAIL_ADDRESS>
Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan

## **References**

**Author details**

**References**

Tokyo, Bunkyo-ku, Tokyo, Japan

380 Artificial Intelligence - Emerging Trends and Applications

PMID: 27730415

Masakazu Sato\*, Kaori Koga, Tomoyuki Fujii and Yutaka Osuga

Department of Obstetrics and Gynecology, Graduate School of Medicine, The University of

[1] Ravi D, Wong C, Deligianni F, Berthelot M, Andreu Perez J, Lo B, et al. Deep learning for health informatics. IEEE Journal of Biomedical and Health Informatics. Jan 2017;**21**(1):

[2] Scholkopf B. Artificial intelligence: Learning to see and act. Nature. 2015;**518**(7540):486-487.

[3] Zhang YC, Kagen AC. Machine learning Interface for medical image analysis. Journal of Digital Imaging. Oct 2017;**30**(5):615-621. DOI: 10.1007/s10278-016-9910-0. PubMed

[4] Gibney E. DeepMind algorithm beats people at classic video games. Nature. 2015;**518**(7540):

465-466. DOI: 10.1038/518465a. Epub 2015/02/27, PubMed PMID: 25719643

4-21. DOI: 10.1109/JBHI.2016.2636665. PubMed PMID: 28055930

DOI: 10.1038/518486a. Epub 2015/02/27, PubMed PMID: 25719660

\*Address all correspondence to<EMAIL_ADDRESS>

**Chapter 19**

**Provisional chapter**

**Application of AI in Modeling of Real System in**

**Application of AI in Modeling of Real System in**

DOI: 10.5772/intechopen.75602

In recent years, discharge of synthetic dye waste from different industries leading to aquatic and environmental pollution is a serious global problem of great concern. Hence, the removal of dye prediction plays an important role in wastewater management and conservation of nature. Artificial intelligence methods are popular owing due to its ease of use and high level of accuracy. This chapter proposes a detailed review of artificial intelligence-based removal dye prediction methods particularly multiple linear regression (MLR), artificial neural networks (ANNs), and least squares-support vector machine (LS-SVM). Furthermore, this chapter will focus on ensemble prediction models (EPMs) used for removal dye prediction. EPMs improve the prediction accuracy by integrating several prediction models. The principles, advantages, disadvantages, and applications of these artificial intelligence-based methods are explained in this chapter. Furthermore, future directions of the research on

artificial intelligence-based removal dye prediction methods are discussed.

**Keywords:** multiple linear regression (MLR), artificial neural networks (ANNs), least

Recently, pollution of water sources by various contaminants becomes a global environmental issue [1]. Among different types of water contaminants, dyes as part of human's life are a major contamination group [2]. Dyes are widely used as coloring agents in the textile, plastics, paper, leather, food, antiseptics, cosmetics, fungicides, and so forth and can be entered to the environment through colored wastewater from these industries [3]. However, during the coloration processes, a significant amount (20–50%) of these dyes is lost and released into

> © 2016 The Author(s). Licensee InTech. 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.

© 2018 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.

**Chemistry**

**Abstract**

**1. Introduction**

**Chemistry**

M. H. Ahmadi Azqhandi and M. Shekari

M. H. Ahmadi Azqhandi and M. Shekari

http://dx.doi.org/10.5772/intechopen.75602

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

squares-support vector regression (LS-SVM)

#### **Application of AI in Modeling of Real System in Chemistry Application of AI in Modeling of Real System in Chemistry**

DOI: 10.5772/intechopen.75602

#### M. H. Ahmadi Azqhandi and M. Shekari M. H. Ahmadi Azqhandi and M. Shekari

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.75602

#### **Abstract**

In recent years, discharge of synthetic dye waste from different industries leading to aquatic and environmental pollution is a serious global problem of great concern. Hence, the removal of dye prediction plays an important role in wastewater management and conservation of nature. Artificial intelligence methods are popular owing due to its ease of use and high level of accuracy. This chapter proposes a detailed review of artificial intelligence-based removal dye prediction methods particularly multiple linear regression (MLR), artificial neural networks (ANNs), and least squares-support vector machine (LS-SVM). Furthermore, this chapter will focus on ensemble prediction models (EPMs) used for removal dye prediction. EPMs improve the prediction accuracy by integrating several prediction models. The principles, advantages, disadvantages, and applications of these artificial intelligence-based methods are explained in this chapter. Furthermore, future directions of the research on artificial intelligence-based removal dye prediction methods are discussed.

**Keywords:** multiple linear regression (MLR), artificial neural networks (ANNs), least squares-support vector regression (LS-SVM)

## **1. Introduction**

Recently, pollution of water sources by various contaminants becomes a global environmental issue [1]. Among different types of water contaminants, dyes as part of human's life are a major contamination group [2]. Dyes are widely used as coloring agents in the textile, plastics, paper, leather, food, antiseptics, cosmetics, fungicides, and so forth and can be entered to the environment through colored wastewater from these industries [3]. However, during the coloration processes, a significant amount (20–50%) of these dyes is lost and released into

© 2016 The Author(s). Licensee InTech. 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. © 2018 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.

the environment as colored wastewater. However, due to toxicity, carcinogenicity, mutagenic and teratogenic properties, and a long-standing environmental pollution, contaminants have become a great environmental concern with potential adverse effects to human health [4]. Also, it can enter the body via the pulmonary system or the digestive system by ingesting contaminated water or food [5–7]. Even though they can be influenced on the photosynthesis process through reduce light penetration and result in reduction oxygen levels in water and, in severe case, consequential the suffocation of aquatic flora and fauna.

Therefore, it is necessary to be removed from wastewater before discharging into bodies of water. Though a number of processes such as ozonation, filtration, membrane, coagulation, precipitation, adsorption, electrochemical techniques, and biosorption have been applied to treat colored textile wastewater from aqueous media [8–11]. However, most of the mentioned methods have shown various restrictions including generation of huge amounts of sludge by the means of electrochemical and chemical coagulation processes or request to high technology with high cost in the membrane technology and advanced oxidation process [12]. Among different treatment methods, adsorption has found particular attention from the researchers worldwide due to the fact that the adsorption is an easy operating, effective, single, and costeffective option for pollutant removal from aqueous environment.

Although the adsorption is an easy operating technique, it is known as a complicated process in the chemistry and dependent on several factors which have a direct impact on the process performance. Thus, it is vital to select an appropriate mathematical model for optimizing and predicting the removal process. The modeling and optimization of adsorption are still in the stage of research. Commonly used models for describing kinematic and/or equilibrium studies (e.g., second-order models or Langmuir, Freundlich) may be inadequate in determining the relationship between factors and evaluating their effect on the absorption process. Optimization is a way for determining the best solution in terms of certain quality criteria, such as process efficiency, and results in improving the performance of the process or designed system [13, 14].

The optimization of the adsorption process tries to find out the design and/or environmental parameters at which the adsorption process would give the best efficiency (**Figure 1**) [15].

Adsorption process is a complex process; therefore, due to complexity of the relationship between output and input parameters, it is difficult to be modeled using statistical approaches. Computational intelligence models are often more flexible than statistical models when modeling complex datasets with possible nonlinearities or missing data [18]. Recently, powerful AI prediction method, such as random forest (RF), adaptive neuro-fuzzy inference system (ANFIS), least square-support vector regression (LS-SVR), radial basis function neural network (RBF-NN), boosted regression tree (BRT), and artificial neural network (ANN) in modeling adsorption process have been successfully used [18–24]. AI is the branch of computer science concerned with making computers behave like humans. The term was coined in 1956 by John McCarthy at the Massachusetts Institute of Technology; it can be employed to explain and model many complex chemical systems because of its reliability, robustness, simplicity, and nonlinearity. AI approach can be learned from experimental data to solve of the complex nonlinear, multidimensional func-

Application of AI in Modeling of Real System in Chemistry

http://dx.doi.org/10.5772/intechopen.75602

385

The previous reviews of adsorption procedure and engineering applications of AI confirmed

The two main objectives of this study are (i) summarizing research on the absorption of dyes

tional relationships without any prior assumptions about their nature [25].

**Figure 1.** Framework for AI-based prediction model.

that there is no specific review on the usage of AI for adsorption process.

by AI models and (ii) providing more research needs for AIs for dye absorption.

Typically, experiments are carried out in a way that one factor is applied and then analyzed, while other factors remain unaffected. In the usual way, one-factor-at-a-time approach is generally time-consuming, and it is impossible to achieve optimal desirability due to the need for a large number of experiments and lack of interaction among factors. This approach is time-consuming; the researcher must screen all the variables independently and require a large number of tests, leading to high cost of study. In addition, a variable in time does not include interactions between selected parameters. This method is called one variable at time (OVAT) [16].

Multivariate statistics techniques (MST) can significantly reduce the number of experiments and explanations of the independent variable (in combination or individually) in the process. MST helps to develop and optimize the operating system, significantly reducing the cost of testing [17].

**Figure 1.** Framework for AI-based prediction model.

the environment as colored wastewater. However, due to toxicity, carcinogenicity, mutagenic and teratogenic properties, and a long-standing environmental pollution, contaminants have become a great environmental concern with potential adverse effects to human health [4]. Also, it can enter the body via the pulmonary system or the digestive system by ingesting contaminated water or food [5–7]. Even though they can be influenced on the photosynthesis process through reduce light penetration and result in reduction oxygen levels in water and,

Therefore, it is necessary to be removed from wastewater before discharging into bodies of water. Though a number of processes such as ozonation, filtration, membrane, coagulation, precipitation, adsorption, electrochemical techniques, and biosorption have been applied to treat colored textile wastewater from aqueous media [8–11]. However, most of the mentioned methods have shown various restrictions including generation of huge amounts of sludge by the means of electrochemical and chemical coagulation processes or request to high technology with high cost in the membrane technology and advanced oxidation process [12]. Among different treatment methods, adsorption has found particular attention from the researchers worldwide due to the fact that the adsorption is an easy operating, effective, single, and cost-

Although the adsorption is an easy operating technique, it is known as a complicated process in the chemistry and dependent on several factors which have a direct impact on the process performance. Thus, it is vital to select an appropriate mathematical model for optimizing and predicting the removal process. The modeling and optimization of adsorption are still in the stage of research. Commonly used models for describing kinematic and/or equilibrium studies (e.g., second-order models or Langmuir, Freundlich) may be inadequate in determining the relationship between factors and evaluating their effect on the absorption process. Optimization is a way for determining the best solution in terms of certain quality criteria, such as process efficiency, and results in improving the performance of the process

The optimization of the adsorption process tries to find out the design and/or environmental parameters at which the adsorption process would give the best efficiency (**Figure 1**) [15].

Typically, experiments are carried out in a way that one factor is applied and then analyzed, while other factors remain unaffected. In the usual way, one-factor-at-a-time approach is generally time-consuming, and it is impossible to achieve optimal desirability due to the need for a large number of experiments and lack of interaction among factors. This approach is time-consuming; the researcher must screen all the variables independently and require a large number of tests, leading to high cost of study. In addition, a variable in time does not include interactions between selected parameters. This method is called one variable at time

Multivariate statistics techniques (MST) can significantly reduce the number of experiments and explanations of the independent variable (in combination or individually) in the process. MST helps to develop and optimize the operating system, significantly reducing the cost of

in severe case, consequential the suffocation of aquatic flora and fauna.

384 Artificial Intelligence - Emerging Trends and Applications

effective option for pollutant removal from aqueous environment.

or designed system [13, 14].

(OVAT) [16].

testing [17].

Adsorption process is a complex process; therefore, due to complexity of the relationship between output and input parameters, it is difficult to be modeled using statistical approaches. Computational intelligence models are often more flexible than statistical models when modeling complex datasets with possible nonlinearities or missing data [18]. Recently, powerful AI prediction method, such as random forest (RF), adaptive neuro-fuzzy inference system (ANFIS), least square-support vector regression (LS-SVR), radial basis function neural network (RBF-NN), boosted regression tree (BRT), and artificial neural network (ANN) in modeling adsorption process have been successfully used [18–24]. AI is the branch of computer science concerned with making computers behave like humans. The term was coined in 1956 by John McCarthy at the Massachusetts Institute of Technology; it can be employed to explain and model many complex chemical systems because of its reliability, robustness, simplicity, and nonlinearity. AI approach can be learned from experimental data to solve of the complex nonlinear, multidimensional functional relationships without any prior assumptions about their nature [25].

The previous reviews of adsorption procedure and engineering applications of AI confirmed that there is no specific review on the usage of AI for adsorption process.

The two main objectives of this study are (i) summarizing research on the absorption of dyes by AI models and (ii) providing more research needs for AIs for dye absorption.

## **2. AI definition**

AI is a subcategory of computer science. Its goal is to enable the development of computers that are able to do things normally done by people. The Stanford researcher, John McCarthy, was named, in 1956, at the current Dartmouth conference, where the mission of the AI field was defined. If we start with this definition, each program can be considered as AI if it does something that we usually think as intelligent in humans.

As the first reported work was in 2000, conducted by Annadurai. Annadurai [29] developed regression models to predict Direct Scarlet B. The inputs for the regression models include the pH, temperature, and the particle size. The proposed models showed promising features to be easy and efficient forecast tools for calculating removal percentage dye from aqueous solution.  Their results indicated that

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387

Ease of use is accounted as one of the main advantages of the MLR method because no parameter needs to be adjusted. Meanwhile, since no detailed physical information is required, this method is efficient and cost-effective. Nonetheless, the MLR is the main constraint due to the inability to deal with nonlinear problems, although the previous research has proven that the

ANNs are computing systems inspired by the biological neural networks that constitute animal brains [25, 49]. An ANN is based on a collection of connected units or nodes called artificial neurons (analogous to biological neurons in an animal brain). Each connection between artificial neurons can transmit a signal from one to another. The artificial neuron (AN) that receives the signal can process it and then signal artificial neurons connected to it. ANs typically have a weight that increases or decreases the strength of the signal at a connection. Signals travel from the first input, to the last output layer, possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. Over time, attention is focused on matching-specific mental abilities, leading to deviations from biology. ANNs have been used on a variety of tasks, including speech recognition, computer vision, social network filtering, machine translation,

In the past two decades, many studies have been carried out to predict various types of decolorization dye from aqueous solution, such as electrocoagulation process [49], Fenton process [50], and adsorption [51] by applying ANNs. M. Ahmadi and Kh. Naderi applied general regression neural network (GRNN) to predict the removal of methylene blue (MB) and Basic Yellow 28 (BY28) from aqueous solution. Their findings indicated that a well-designed GRNN is able to predict the removal of azo dye based on sonication time, initial dye concentration, and adsorbent mass. Ahmadi and J. Pooralhossini used backpropagation neural network (BPNN) to predict the decolorization of sunset yellow (SY) and disulfine blue (DB) [52]. The obtained results show that the BPNN model outperforms the classical statistical model in terms of R<sup>2</sup>

RMSE, MAE, and AAD for both dyes. Ahmadi and team used BPNN to predict the efficiency of two carcinogenic dye (methylene blue (MB) and malachite green (MG)) adsorption onto Mn@ CuS/ZnS nanocomposite-loaded activated carbon (Mn@ CuS/ZnS-NC-AC) as a novel adsorbent to identify the model parameters in order to improve the prediction performance [35]. Ahmadi and Dastkhoon used neural network to predict Safranin-O (SO) and indigo carmine (IC) adsorption onto Ni:FeO(OH)-NWs-AC. In this work, the influence of process variables (initial dye concentration, adsorbent mass, and sonication time) on the removal of both dyes was investigated by central composite rotatable design (CCRD) of RSM, multilayer perceptron (MLP) neural network, and Doolittle factorization algorithm (DFA). The ANN model

,

MLR can be used as an efficient tool for predicting parentage removal [10, 25, 36–48].

**2.2. Artificial neural network (ANN)**

playing board and video games, and medical diagnosis.

the proposed method cannot well predict the removal dye percentage [13, 30–35].

The typical AI-based prediction method consists of four main steps. The first step is to acquire input and output data. Input data are those aspects that affect or relate to output data. These aspects include but are not limited to pH, sonication time, adsorbent dose, temperature, and initial concentration of contaminant. The output data is removal percentage. The next step is to preprocess the collected data in an appropriate format before using them to train the forecast model. Some data pre-processing techniques such as data normalization, data transfer, and data interpolation are applied at this stage to improve data quality and reduce negative impact. When the data is ready, the third step is to train the prediction model.

Since the crucial concept of empirical modeling is learning from historical data, a training process is essential for the development of the model. This step is achieved by selecting the appropriate parameters for the model. The type of parameters is determined by the algorithms selected by the researcher, while selecting the proper parameter can guarantee the performance of the model. The last step is testing the model. At this stage, the data test is examined to test the prediction of model performance. Performance indicators such as RMSE, R2 , MAE, and AAD are used to evaluate performance.

The AI can be more classified into four types (i.e., multiple linear regression (MLR), artificial neural network (ANN), least square-support vector machine (LS-SVM), and ensemble prediction models) based on the learning algorithms; the following part of this section describes main techniques used for AI-based prediction model.

## **2.1. Multiple linear regression**

MLR is a statistical technique that uses several explanatory variables to predict a response variable. The goal of MLR is to model the relationship between the input and response variables [26, 27].

The model for MLR, given n observations, is

$$y\_i = B\_0 + B\_1 \mathbf{x}\_{\text{in}} + B\_2 \mathbf{x}\_{\text{in}} + \dots + B\_p \mathbf{x}\_{ip} + \mathbf{E} \quad \text{where } \text{i } \epsilon \text{(1:n)}. \tag{1}$$

The response surface methodology (RSM) is the most popular methods used in the absorption research literature. RSM determine the mathematical relation between parameters and responses. Modeling or model fitting in RSM consists of two steps: coding of experimental data and regression. For the first, input and output data was coded by using the general equation due to RSM operate on coded input values like +1, 0, and − 1 instead of actual values [28].

In the next step, coded experimental data are fitted to a selected model using multiple linear regression (MLR). In spite of the simplicity of MLR, it was used recently in the removal processes. As the first reported work was in 2000, conducted by Annadurai. Annadurai [29] developed regression models to predict Direct Scarlet B. The inputs for the regression models include the pH, temperature, and the particle size. The proposed models showed promising features to be easy and efficient forecast tools for calculating removal percentage dye from aqueous solution.  Their results indicated that the proposed method cannot well predict the removal dye percentage [13, 30–35].

Ease of use is accounted as one of the main advantages of the MLR method because no parameter needs to be adjusted. Meanwhile, since no detailed physical information is required, this method is efficient and cost-effective. Nonetheless, the MLR is the main constraint due to the inability to deal with nonlinear problems, although the previous research has proven that the MLR can be used as an efficient tool for predicting parentage removal [10, 25, 36–48].

## **2.2. Artificial neural network (ANN)**

**2. AI definition**

386 Artificial Intelligence - Emerging Trends and Applications

R2

AI is a subcategory of computer science. Its goal is to enable the development of computers that are able to do things normally done by people. The Stanford researcher, John McCarthy, was named, in 1956, at the current Dartmouth conference, where the mission of the AI field was defined. If we start with this definition, each program can be considered as AI if it does

The typical AI-based prediction method consists of four main steps. The first step is to acquire input and output data. Input data are those aspects that affect or relate to output data. These aspects include but are not limited to pH, sonication time, adsorbent dose, temperature, and initial concentration of contaminant. The output data is removal percentage. The next step is to preprocess the collected data in an appropriate format before using them to train the forecast model. Some data pre-processing techniques such as data normalization, data transfer, and data interpolation are applied at this stage to improve data quality and reduce negative

Since the crucial concept of empirical modeling is learning from historical data, a training process is essential for the development of the model. This step is achieved by selecting the appropriate parameters for the model. The type of parameters is determined by the algorithms selected by the researcher, while selecting the proper parameter can guarantee the performance of the model. The last step is testing the model. At this stage, the data test is examined to test the prediction of model performance. Performance indicators such as RMSE,

The AI can be more classified into four types (i.e., multiple linear regression (MLR), artificial neural network (ANN), least square-support vector machine (LS-SVM), and ensemble prediction models) based on the learning algorithms; the following part of this section describes

MLR is a statistical technique that uses several explanatory variables to predict a response variable. The goal of MLR is to model the relationship between the input and response vari-

*yi* = *B*<sup>0</sup> + *B*<sup>1</sup> *xi*<sup>1</sup> + *B*<sup>2</sup> *xi*<sup>2</sup> + …+*Bp xip* + E where i ϵ[1:*n*]. (1)

The response surface methodology (RSM) is the most popular methods used in the absorption research literature. RSM determine the mathematical relation between parameters and responses. Modeling or model fitting in RSM consists of two steps: coding of experimental data and regression. For the first, input and output data was coded by using the general equation due to RSM operate on coded input values like +1, 0, and − 1 instead of actual values [28]. In the next step, coded experimental data are fitted to a selected model using multiple linear regression (MLR). In spite of the simplicity of MLR, it was used recently in the removal processes.

impact. When the data is ready, the third step is to train the prediction model.

something that we usually think as intelligent in humans.

, MAE, and AAD are used to evaluate performance.

main techniques used for AI-based prediction model.

The model for MLR, given n observations, is

**2.1. Multiple linear regression**

ables [26, 27].

ANNs are computing systems inspired by the biological neural networks that constitute animal brains [25, 49]. An ANN is based on a collection of connected units or nodes called artificial neurons (analogous to biological neurons in an animal brain). Each connection between artificial neurons can transmit a signal from one to another. The artificial neuron (AN) that receives the signal can process it and then signal artificial neurons connected to it. ANs typically have a weight that increases or decreases the strength of the signal at a connection. Signals travel from the first input, to the last output layer, possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. Over time, attention is focused on matching-specific mental abilities, leading to deviations from biology. ANNs have been used on a variety of tasks, including speech recognition, computer vision, social network filtering, machine translation, playing board and video games, and medical diagnosis.

In the past two decades, many studies have been carried out to predict various types of decolorization dye from aqueous solution, such as electrocoagulation process [49], Fenton process [50], and adsorption [51] by applying ANNs. M. Ahmadi and Kh. Naderi applied general regression neural network (GRNN) to predict the removal of methylene blue (MB) and Basic Yellow 28 (BY28) from aqueous solution. Their findings indicated that a well-designed GRNN is able to predict the removal of azo dye based on sonication time, initial dye concentration, and adsorbent mass. Ahmadi and J. Pooralhossini used backpropagation neural network (BPNN) to predict the decolorization of sunset yellow (SY) and disulfine blue (DB) [52]. The obtained results show that the BPNN model outperforms the classical statistical model in terms of R<sup>2</sup> , RMSE, MAE, and AAD for both dyes. Ahmadi and team used BPNN to predict the efficiency of two carcinogenic dye (methylene blue (MB) and malachite green (MG)) adsorption onto Mn@ CuS/ZnS nanocomposite-loaded activated carbon (Mn@ CuS/ZnS-NC-AC) as a novel adsorbent to identify the model parameters in order to improve the prediction performance [35]. Ahmadi and Dastkhoon used neural network to predict Safranin-O (SO) and indigo carmine (IC) adsorption onto Ni:FeO(OH)-NWs-AC. In this work, the influence of process variables (initial dye concentration, adsorbent mass, and sonication time) on the removal of both dyes was investigated by central composite rotatable design (CCRD) of RSM, multilayer perceptron (MLP) neural network, and Doolittle factorization algorithm (DFA). The ANN model was found to be more precise than the other models. We performed the sensitivity analysis (by using of weight neuron) and confirmed that sonication time has the essential factor affecting the removal of SO and IC [33]. Ahmadi et al. developed a BP neural network model and partial least squares (PLS) to predict the ultrasonic-assisted simultaneous removal of fast green (FG), eosin Y (EY), and quinine yellow (QY) from aqueous media following the use of MOF-5 as a metal organic framework and activated carbon hybrid (AC-MOF-5). The obtained results show that ANN and PLS model is a powerful tool for prediction of under-study dye adsorption by AC-MOF-5 [53].

methylene blue adsorption onto copper oxide nanoparticle loaded on activated carbon (CuO-NP-AC) by Ghaedi et al. Both studies indicate that SVM has a better performance in building

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The main advantage of LS-SVM that was introduced by Foucquier et al. [58] is based on the structural risk minimization principle which aims to minimize the upper bound of the general error consisting of the sum of the training error. Also, SVM provides a better balance between prediction accuracy and computation speed comparing with ANNs and RSM [34]. The limitation of SVM method is the determination of kernel function. There is no uniform standard for determining which kernel will result in the most accurate SVM. Researchers have to determine the kernel function based on the characteristics of the data as well as their

Whereof each prediction method has its own limitations, currently, a trend has evolved to introduce new mathematical methods, called ensemble learning. These methods have been more extensive in analytical chemistry for complex data analysis. The adaptability of data mining methods makes them able to deal with typical problems: too many descriptors in the model, mixtures of different data types, complex data having missing values, multiple classes, or unbalanced data sets. For instance, some data mining methods such as support vector machine [34] and artificial neural networks (ANNs) [59] have been applied in different realms of science and engineering. The Ensemble prediction has become increasingly popular

This method differs from each different prediction method because it creates a composite model which integrates different individual prediction models. Instead of a prediction algorithm, this method acts as a framework to reduce prediction errors by combining different reduction algorithms together. Ensemble prediction methods have been successfully applied in several areas of chemistry that they have large data volumes, including spectroscopy [62, 63], quantitative structure–activity relationship (QSAR) modeling [64, 65], and omics sciences [66]. Ensemble prediction is able to manage with many types of responses and predictors such as categorical or numeric and loss functions such as Laplace, Gaussian, Poisson, and Bernoulli [67]. De'ath [68] has shown that ensemble prediction methods, unlike many other regression methods, can be used for both prediction and explanation of the underlying relationships

The two main outputs of an ensemble prediction model which are the partial dependence plots and the variable importance rankings can be used together for model interpretation. Friedman [69] has proposed that while these outputs might not offer a complete description, they can at least give an insight of the relation between the response and the predictors.

Some authors (e.g., Hastie et al.) [70] have shown that ensemble prediction is one of the most powerful machine/statistical learning ideas than have been presented during the 1990s, and

model and prediction than other AI-based prediction methods [57].

*2.3.1. Advantages and limitations*

*2.3.2. Ensemble prediction models*

own experience.

in chemistry [60, 61].

between response and predictors.

The main advantage of ANN method is its ability to detect complex nonlinear relationship between the inputs and outputs that this characteristic makes it possible to be applied for real systems. However, ANN method fails to establish any interconnection relationship between building physical parameters and removal percentage, which limits the model's fitting ability.

## **2.3. Least square-support vector machine (LS-SVM)**

SVM as a learning method was developed by Vapnik and is a powerful tool [34, 54]. This supervised learning method can be used for regression or classification in nonlinear models, and density estimation leads to complex optimization problems, typically quadratic programming. However, this method (SVM) is often time-consuming and difficult to adapt, suffering from the problem of a large memory requirement and CPU time when trained in batch mode. This limitation is overcome by LS-SVM as the modified version of SVM which solves the set of linear equations instead of the quadratic programming problem to minimize the complex nature of the optimization processes. The theory and more details of SVM and LS-SVM can be found in the literature.

Liang and team first applied LS-SVM in the area of water quality measurements in 2011. They showed that the model output made a relatively good training fitting effect and the predict effect was relatively satisfactory. The model has not only a good learning accuracy but a good generalization ability. The predictive fitting precision of the test data set was more than 90%, and the prediction error is minimum, and RMSE is 0.0028 [55].

Similarly, our group used LS-SVM for the optimization and/or modeling of pH, ZnS-NPs-AC mass, MB concentration, and sonication time to develop respective predictive equations for the simulation of the efficiency of MB adsorption. The obtained results using LS-SVM exhibit a nonlinear approach which shows better performances in comparison to central composite design (CCD) for the prediction of MB adsorption [34].

Niyaz Mohammad Mahmoodi and team used of least square-support vector machine (LS-SVM) to model the dye removal. The graphical plots and the values of statistical parameter showed LS-SVM as an intelligent model suitable for modeling of dye adsorption [56].

Our group compared LS-SVM with other AI-based prediction methods in removal dye prediction. We compared SVR with several ANN models for prediction of the adsorption of methylene blue (MB) from aqueous solutions by zinc sulfide nanoparticles with activated carbon (ZnS-NPs-AC). Also, a multiple linear regression (MLR) model and LS-SVM model with principal component analysis (PCA) were used for pre-processing to predict the efficiency of methylene blue adsorption onto copper oxide nanoparticle loaded on activated carbon (CuO-NP-AC) by Ghaedi et al. Both studies indicate that SVM has a better performance in building model and prediction than other AI-based prediction methods [57].

## *2.3.1. Advantages and limitations*

was found to be more precise than the other models. We performed the sensitivity analysis (by using of weight neuron) and confirmed that sonication time has the essential factor affecting the removal of SO and IC [33]. Ahmadi et al. developed a BP neural network model and partial least squares (PLS) to predict the ultrasonic-assisted simultaneous removal of fast green (FG), eosin Y (EY), and quinine yellow (QY) from aqueous media following the use of MOF-5 as a metal organic framework and activated carbon hybrid (AC-MOF-5). The obtained results show that ANN and PLS model is a powerful tool for prediction of under-study dye

The main advantage of ANN method is its ability to detect complex nonlinear relationship between the inputs and outputs that this characteristic makes it possible to be applied for real systems. However, ANN method fails to establish any interconnection relationship between building physical parameters and removal percentage, which limits the model's fitting ability.

SVM as a learning method was developed by Vapnik and is a powerful tool [34, 54]. This supervised learning method can be used for regression or classification in nonlinear models, and density estimation leads to complex optimization problems, typically quadratic programming. However, this method (SVM) is often time-consuming and difficult to adapt, suffering from the problem of a large memory requirement and CPU time when trained in batch mode. This limitation is overcome by LS-SVM as the modified version of SVM which solves the set of linear equations instead of the quadratic programming problem to minimize the complex nature of the optimization processes. The theory and more details of SVM and LS-SVM can

Liang and team first applied LS-SVM in the area of water quality measurements in 2011. They showed that the model output made a relatively good training fitting effect and the predict effect was relatively satisfactory. The model has not only a good learning accuracy but a good generalization ability. The predictive fitting precision of the test data set was more than 90%,

Similarly, our group used LS-SVM for the optimization and/or modeling of pH, ZnS-NPs-AC mass, MB concentration, and sonication time to develop respective predictive equations for the simulation of the efficiency of MB adsorption. The obtained results using LS-SVM exhibit a nonlinear approach which shows better performances in comparison to central composite

Niyaz Mohammad Mahmoodi and team used of least square-support vector machine (LS-SVM) to model the dye removal. The graphical plots and the values of statistical parameter showed LS-SVM as an intelligent model suitable for modeling of dye adsorption [56]. Our group compared LS-SVM with other AI-based prediction methods in removal dye prediction. We compared SVR with several ANN models for prediction of the adsorption of methylene blue (MB) from aqueous solutions by zinc sulfide nanoparticles with activated carbon (ZnS-NPs-AC). Also, a multiple linear regression (MLR) model and LS-SVM model with principal component analysis (PCA) were used for pre-processing to predict the efficiency of

adsorption by AC-MOF-5 [53].

388 Artificial Intelligence - Emerging Trends and Applications

be found in the literature.

**2.3. Least square-support vector machine (LS-SVM)**

and the prediction error is minimum, and RMSE is 0.0028 [55].

design (CCD) for the prediction of MB adsorption [34].

The main advantage of LS-SVM that was introduced by Foucquier et al. [58] is based on the structural risk minimization principle which aims to minimize the upper bound of the general error consisting of the sum of the training error. Also, SVM provides a better balance between prediction accuracy and computation speed comparing with ANNs and RSM [34]. The limitation of SVM method is the determination of kernel function. There is no uniform standard for determining which kernel will result in the most accurate SVM. Researchers have to determine the kernel function based on the characteristics of the data as well as their own experience.

#### *2.3.2. Ensemble prediction models*

Whereof each prediction method has its own limitations, currently, a trend has evolved to introduce new mathematical methods, called ensemble learning. These methods have been more extensive in analytical chemistry for complex data analysis. The adaptability of data mining methods makes them able to deal with typical problems: too many descriptors in the model, mixtures of different data types, complex data having missing values, multiple classes, or unbalanced data sets. For instance, some data mining methods such as support vector machine [34] and artificial neural networks (ANNs) [59] have been applied in different realms of science and engineering. The Ensemble prediction has become increasingly popular in chemistry [60, 61].

This method differs from each different prediction method because it creates a composite model which integrates different individual prediction models. Instead of a prediction algorithm, this method acts as a framework to reduce prediction errors by combining different reduction algorithms together. Ensemble prediction methods have been successfully applied in several areas of chemistry that they have large data volumes, including spectroscopy [62, 63], quantitative structure–activity relationship (QSAR) modeling [64, 65], and omics sciences [66]. Ensemble prediction is able to manage with many types of responses and predictors such as categorical or numeric and loss functions such as Laplace, Gaussian, Poisson, and Bernoulli [67]. De'ath [68] has shown that ensemble prediction methods, unlike many other regression methods, can be used for both prediction and explanation of the underlying relationships between response and predictors.

The two main outputs of an ensemble prediction model which are the partial dependence plots and the variable importance rankings can be used together for model interpretation. Friedman [69] has proposed that while these outputs might not offer a complete description, they can at least give an insight of the relation between the response and the predictors.

Some authors (e.g., Hastie et al.) [70] have shown that ensemble prediction is one of the most powerful machine/statistical learning ideas than have been presented during the 1990s, and it has been suggested [71, 72] that the application of ensemble prediction to classification and regression trees results in individual classifiers (e.g., classification trees, regression trees) which generally are competitive with any other method.

different types of predictor variables. In addition, they have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and can automatically handle interaction effects between predictors. However, compared with other predictive methods, ensemble models require more time to calculate and a high level of knowledge as a combination of different base models. Another disadvantage of the model is the fact that its predictive function depends on the selection of base models. In the previous study, the researchers selected the base model based on their previous knowledge. There is a lack of approach to determine which base model should be considered and included in the ensemble model.

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According to previous researches, each type of AI-based prediction method has its own disadvantages and advantages; thus scientists have to select suitable method to solve their problems. For example, MLR is more appropriate than other methods in predicting removal dye because of its ease of use and high calculation speed. While LS-SVM and ANNs are more suitable for real system with high nonlinearity because of their high level of prediction accuracy. On the other hand, some researchers have tried to compare AI-based prediction methods with other methods in removal dye prediction. For example, Tanzifi [74] compared ANNs with RSM for predicting removal Amido Black 10B. The comparison of the adsorption efficiencies obtained by the ANN model and the experimental data evidenced that the ANN model could estimate the behavior of the Amido Black 10B dye adsorption process under various conditions. Their study proposed ANN model as a simpler and more efficient building energy prediction tool when compared with energy simulation software. Based on these researches, the advantages and disadvantages of AI-based prediction methods are summarized below.

**1.** Comparing AI with other engineering approaches shows that the building and/or development of the AI-based prediction methods does not need any detailed physical information

**2.** Based on previous study, if model is well trained, AI methods give promising prediction

**3.** The data gaining and data loading process is relatively simple, which means the prediction

There are many published papers on the prediction of dye adsorption using OVAT. However, in this chapter, we review the important research studies on dye adsorption forecasting using

which in return saves both cost and time for leading the prediction.

**3. Discussion**

**3.1. Advantages**

accuracy.

**4. Conclusion**

The advantages of AI model are:

model can be easily obtained.

In addition, Breiman [71] shows that applying ensemble prediction to classification and regression tree can actually be much quicker than fitting a neural net classifier.

Due to the high prediction accuracy, ensemble learning method has become a favorable topic in recent years and has already been applied to many fields successfully. For example, our group [73] developed boosted regression tree (BRT), an ensemble method for fitting statistical models that differ fundamentally from conventional techniques that aim to fit a single parsimonious model. In this study, response surface methodology (RSM), artificial neural network (ANN), and BRT have been used for the optimization and/or modeling of stirred time (min), pH, adsorbent mass (mg), and concentrations of MB and Cd2+ ions (mg L−1) to develop respective predictive equations for simulation of the efficiency of MB and Cd2+ adsorption based on experimental data set achieved in batch study. All three models showed good predictions in this study. But the BRT model was more precise than the other models, and it showed that BRT is a powerful tool for modeling and optimizing removal of MB and Cd(II).

Similarly, our group [32] used adaptive network-based fuzzy inference system (ANFIS) ensemble models as a support tool for examining data and making prediction to recognize and predict the removal percentage in MB and SY dye solution of different concentrations. The predictive capabilities of MLR and ANFIS are compared in terms of square correlation coefficient (R<sup>2</sup> ), root mean square error (RMSE), mean absolute error (MAE), and absolute average deviation (AAD) against the empirical data. It is found that the ANFIS model shows the better prediction accuracy than the CCD model.

In another work by our group [13], random forest (RF) and response surface methodology (RSM) were used to model and predict the efficiency of malachite green removal from aqueous solution by ultrasound-assisted adsorption onto the silver hydroxide nanoparticles loaded on activated carbon (AgOH-NPs-AC). The parameters such as pH, initial MG concentration, sonication time, and adsorbent dosage involved in the adsorption process were set within the ranges 2.0–10, 4–20 mg L−1, 2–6 min, and 0.005–0.025 g, respectively. The performance of the RF and CCD models for the description of experimental data was evaluated in terms of R<sup>2</sup> , RMSE, MAE, and AAD. The obtained results showed that the RF model outperformed classical statistical model for modeling the process of dye adsorption.

Also, ensemble prediction approach (i.e., radial basis function neural network (RBF-NN) and random forest (RF)) was developed and evaluated against a quadratic response surface model to predict the maximum removal efficiency of brilliant green (BG) from aqueous media in relation to BG concentration (4–20 mg L−1), sonication time (2–6 min), and ZnS-NP-AC mass (0.010– 0.030 g) by ultrasound-assisted adsorption [31]. All three (i.e., RBF network, RF, and polynomial) models were compared against the experimental data using four statistical indices, namely, R<sup>2</sup> , RMSE, MAE, and AAD. Graphical plots were also used for model comparison. The obtained results using RBF network and RF exhibit a better performance than MLR for both dyes.

The main advantage of the ensemble method is the improvement of accuracy. Also, these methods incorporate important advantages such as accommodating missing data and handling different types of predictor variables. In addition, they have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and can automatically handle interaction effects between predictors. However, compared with other predictive methods, ensemble models require more time to calculate and a high level of knowledge as a combination of different base models. Another disadvantage of the model is the fact that its predictive function depends on the selection of base models. In the previous study, the researchers selected the base model based on their previous knowledge. There is a lack of approach to determine which base model should be considered and included in the ensemble model.

## **3. Discussion**

it has been suggested [71, 72] that the application of ensemble prediction to classification and regression trees results in individual classifiers (e.g., classification trees, regression trees)

In addition, Breiman [71] shows that applying ensemble prediction to classification and

Due to the high prediction accuracy, ensemble learning method has become a favorable topic in recent years and has already been applied to many fields successfully. For example, our group [73] developed boosted regression tree (BRT), an ensemble method for fitting statistical models that differ fundamentally from conventional techniques that aim to fit a single parsimonious model. In this study, response surface methodology (RSM), artificial neural network (ANN), and BRT have been used for the optimization and/or modeling of stirred time (min), pH, adsorbent mass (mg), and concentrations of MB and Cd2+ ions (mg L−1) to develop respective predictive equations for simulation of the efficiency of MB and Cd2+ adsorption based on experimental data set achieved in batch study. All three models showed good predictions in this study. But the BRT model was more precise than the other models, and it showed that

Similarly, our group [32] used adaptive network-based fuzzy inference system (ANFIS) ensemble models as a support tool for examining data and making prediction to recognize and predict the removal percentage in MB and SY dye solution of different concentrations. The predictive capabilities of MLR and ANFIS are compared in terms of square correlation

average deviation (AAD) against the empirical data. It is found that the ANFIS model shows

In another work by our group [13], random forest (RF) and response surface methodology (RSM) were used to model and predict the efficiency of malachite green removal from aqueous solution by ultrasound-assisted adsorption onto the silver hydroxide nanoparticles loaded on activated carbon (AgOH-NPs-AC). The parameters such as pH, initial MG concentration, sonication time, and adsorbent dosage involved in the adsorption process were set within the ranges 2.0–10, 4–20 mg L−1, 2–6 min, and 0.005–0.025 g, respectively. The performance of the RF and CCD models for the description of experimental data was evaluated in terms of R<sup>2</sup>

RMSE, MAE, and AAD. The obtained results showed that the RF model outperformed classi-

Also, ensemble prediction approach (i.e., radial basis function neural network (RBF-NN) and random forest (RF)) was developed and evaluated against a quadratic response surface model to predict the maximum removal efficiency of brilliant green (BG) from aqueous media in relation to BG concentration (4–20 mg L−1), sonication time (2–6 min), and ZnS-NP-AC mass (0.010– 0.030 g) by ultrasound-assisted adsorption [31]. All three (i.e., RBF network, RF, and polynomial) models were compared against the experimental data using four statistical indices, namely, R<sup>2</sup>

RMSE, MAE, and AAD. Graphical plots were also used for model comparison. The obtained

The main advantage of the ensemble method is the improvement of accuracy. Also, these methods incorporate important advantages such as accommodating missing data and handling

results using RBF network and RF exhibit a better performance than MLR for both dyes.

), root mean square error (RMSE), mean absolute error (MAE), and absolute

regression tree can actually be much quicker than fitting a neural net classifier.

BRT is a powerful tool for modeling and optimizing removal of MB and Cd(II).

which generally are competitive with any other method.

390 Artificial Intelligence - Emerging Trends and Applications

the better prediction accuracy than the CCD model.

cal statistical model for modeling the process of dye adsorption.

coefficient (R<sup>2</sup>

According to previous researches, each type of AI-based prediction method has its own disadvantages and advantages; thus scientists have to select suitable method to solve their problems. For example, MLR is more appropriate than other methods in predicting removal dye because of its ease of use and high calculation speed. While LS-SVM and ANNs are more suitable for real system with high nonlinearity because of their high level of prediction accuracy. On the other hand, some researchers have tried to compare AI-based prediction methods with other methods in removal dye prediction. For example, Tanzifi [74] compared ANNs with RSM for predicting removal Amido Black 10B. The comparison of the adsorption efficiencies obtained by the ANN model and the experimental data evidenced that the ANN model could estimate the behavior of the Amido Black 10B dye adsorption process under various conditions. Their study proposed ANN model as a simpler and more efficient building energy prediction tool when compared with energy simulation software. Based on these researches, the advantages and disadvantages of AI-based prediction methods are summarized below.

## **3.1. Advantages**

,

,

The advantages of AI model are:

## **4. Conclusion**

There are many published papers on the prediction of dye adsorption using OVAT. However, in this chapter, we review the important research studies on dye adsorption forecasting using AI methods. The literature survey in this chapter showed that the AI approaches can be successfully used for the modeling and predication of dye adsorption process with acceptable accuracy compared to conventional linear models such as RSM. The future research proposed for AIs in the field of dye removal for carrying out extensive studies are as follows:

[4] Liu C, Omer A. Adsorptive removal of cationic methylene blue dye using carboxymethyl cellulose/k-carrageenan/activated montmorillonite composite beads: Isotherm and kinetic studies. International Journal of Biological Macromolecules. 2018;**106**:823-833 [5] Gao H, Zhao S, Cheng X, Wang X, Zheng L. Removal of anionic azo dyes from aqueous solution using magnetic polymer multi-wall carbon nanotube nanocomposite as adsor-

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[6] Zhu H-Y, Fu Y-Q, Jiang R, Yao J, Xiao L, Zeng G-M. Novel magnetic chitosan/poly (vinyl alcohol) hydrogel beads: Preparation, characterization and application for adsorption of

[7] Madrakian T, Afkhami A, Ahmadi M, Bagheri H. Removal of some cationic dyes from aqueous solutions using magnetic-modified multi-walled carbon nanotubes. Journal of

[8] Verma AK, Dash RR, Bhunia P. A review on chemical coagulation/flocculation technologies for removal of colour from textile wastewaters. Journal of Environmental Mana-

[9] Asgher M, Bhatti HN. Evaluation of thermodynamics and effect of chemical treatments on sorption potential of *Citrus* waste biomass for removal of anionic dyes from aqueous

[10] Körbahti BK, Artut K, Geçgel C, Özer A. Electrochemical decolorization of textile dyes and removal of metal ions from textile dye and metal ion binary mixtures. Chemical

[11] Gupta V. Application of low-cost adsorbents for dye removal—A review. Journal of

[12] Azqhandi MHA, Rajabi F, Keramati M. Synthesis of Cd doped ZnO/CNT nanocomposite by using microwave method: Photocatalytic behavior, adsorption and kinetic study.

[13] Solaymani E, Ghaedi M, Karimi H, Azqhandi A, Hossein M, Asfaram A. Intensified removal of malachite green by AgOH-AC nanoparticles combined with ultrasound:

[14] Amini M, Yazdanbakhsh A, Eslami A, Aghayani E. Optimization of coagulation-flocculation process for dye and COD removal from real dyeing wastewater and evaluation of effluent biodegradability in a carpet factory. Journal of Health in the Field. 2018;**13**(3):24-33

[15] Tuzen M, Sarı A, Saleh TA. Response surface optimization, kinetic and thermodynamic

[16] Sleiman M, Vildozo D, Ferronato C, Chovelon J-M. Photocatalytic degradation of azo dye Metanil yellow: Optimization and kinetic modeling using a chemometric approach.

nanocomposite.

Modeling and optimization. Applied Organometallic Chemistry. 2017:1-12

studies for effective removal of rhodamine B by magnetic AC/CeO<sup>2</sup>

Journal of Environmental Management. 2018;**206**:170-177

Applied Catalysis B: Environmental. 2007;**77**(1):1-11

dye from aqueous solution. Bioresource Technology. 2012;**105**:24-30

bent. Chemical Engineering Journal. 2013;**223**:84-90

solutions. Ecological Engineering. 2012;**38**(1):79-85

Environmental Management. 2009;**90**(8):2313-2342

Engineering Journal. 2011;**173**(3):677-688

Results in Physics. 2017:1106-1114

Hazardous Materials. 2011;**196**:109-114

gement. 2012;**93**(1):154-168

Based on the reported study and discussions presented in this chapter, it can be concluded that AI methods are excellent approaches for the adsorption of dyes. The information offered in this chapter would be highly useful to the scientists working in the field of dye adsorption and AIs in their investigations.

## **Author details**

M. H. Ahmadi Azqhandi\* and M. Shekari

\*Address all correspondence to<EMAIL_ADDRESS>
Applied Chemistry Department, Faculty of Petroleum and Gas (Gachsaran), Yasouj University, Gachsaran, Iran

## **References**

[4] Liu C, Omer A. Adsorptive removal of cationic methylene blue dye using carboxymethyl cellulose/k-carrageenan/activated montmorillonite composite beads: Isotherm and kinetic studies. International Journal of Biological Macromolecules. 2018;**106**:823-833

AI methods. The literature survey in this chapter showed that the AI approaches can be successfully used for the modeling and predication of dye adsorption process with acceptable accuracy compared to conventional linear models such as RSM. The future research proposed

**i.** The prediction capability of other AI models such as group method of data handling (GMDH), random forest (RF), neural gas network, regression tree, and radial basis func-

**ii.** The hybridization of the AIs together such as LS-SVM and GMDH forecasting methods,

**iii.** A few studies have been reported about combining ANN approaches with optimization algorithms. However, it is necessary to extend the optimization of network configuration for modeling adsorption process using an evolutionary computation method such as ant colony, PSO, GA algorithm, tabu search, artificial bee colony, firefly algorithm, teaching-learning-based optimization, harmony search, shuffled frog-leaping algorithm,

Based on the reported study and discussions presented in this chapter, it can be concluded that AI methods are excellent approaches for the adsorption of dyes. The information offered in this chapter would be highly useful to the scientists working in the field of dye adsorption

Applied Chemistry Department, Faculty of Petroleum and Gas (Gachsaran), Yasouj

[1] Silva TL, Cazetta AL, Souza PS, Zhang T, Asefa T, Almeida VC. Mesoporous activated carbon fibers synthesized from denim fabric waste: Efficient adsorbents for removal of textile dye from aqueous solutions. Journal of Cleaner Production. 2018;**171**:482-490 [2] Sarvajith M, Reddy GKK, Nancharaiah Y. Textile dye biodecolourization and ammonium removal over nitrite in aerobic granular sludge sequencing batch reactors. Journal

[3] Li L, Qi G, Wang B, Yue D, Wang Y, Sato T. Fulvic acid anchored layered double hydroxides: A multifunctional composite adsorbent for the removal of anionic dye and toxic

for AIs in the field of dye removal for carrying out extensive studies are as follows:

tion network (RBFN) for dye adsorption needs further research studies.

regarding the potential of predict dye adsorption, was proposed.

simulated annealing, and invasive weed optimization.

\*Address all correspondence to<EMAIL_ADDRESS>
of Hazardous Materials. 2018;**342**:536-543

metal. Journal of Hazardous Materials. 2018;**343**:19-28

and AIs in their investigations.

392 Artificial Intelligence - Emerging Trends and Applications

University, Gachsaran, Iran

M. H. Ahmadi Azqhandi\* and M. Shekari

**Author details**

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**Chapter 20**

**Provisional chapter**

**Application of AI in Chemical Engineering**

**Application of AI in Chemical Engineering**

DOI: 10.5772/intechopen.76027

A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investi-

**Keywords:** chemical engineering, AI algorithms, classification, process control,

Artificial intelligence (AI) applications in chemical engineering have increased dramatically recently. This chapter deals with various applications of artificial intelligence (AI) in the chemical engineering field including process such as modeling, optimization, process control, fault detection and diagnosis. The aim of the chapter is to provide an overview of the field by presenting the capabilities and limitations of using the AI approach, focusing on artificial

It is shown that complexities of conventional approaches when dealing with chemical processes which are inherently highly nonlinear can be tackled through the application of AI

> © 2016 The Author(s). Licensee InTech. 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.

© 2018 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.

Zeinab Hajjar, Shokoufe Tayyebi and Mohammad Hosein Eghbal Ahmadi

Zeinab Hajjar, Shokoufe Tayyebi and Mohammad Hosein Eghbal Ahmadi

http://dx.doi.org/10.5772/intechopen.76027

**Abstract**

**1. Introduction**

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

gated in various chemical engineering fields.

neural network (ANN) and fuzzy logic methods.

methods. Four illustrative relevant examples are also presented.

modeling, optimization, fault detection and diagnosis

#### **Application of AI in Chemical Engineering Application of AI in Chemical Engineering**

DOI: 10.5772/intechopen.76027

Zeinab Hajjar, Shokoufe Tayyebi and Mohammad Hosein Eghbal Ahmadi Zeinab Hajjar, Shokoufe Tayyebi and Mohammad Hosein Eghbal Ahmadi

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.76027

#### **Abstract**

[65] Polishchuk PG, Muratov EN, Artemenko AG, Kolumbin OG, Muratov NN, Kuz'min VE. Application of random forest approach to QSAR prediction of aquatic toxicity.

[66] Brereton RG, Lloyd GR. Support vector machines for classification and regression.

[67] Ridgeway G. Package gbm: Generalized Boosted Regression Models. Version 1.6. 3.2.

[68] De'Ath G. Boosted trees for ecological modeling and prediction. Ecology. 2007;**88**(1):243-251 [69] Friedman JH. Greedy function approximation: A gradient boosting machine. Annals of

[70] Hastie T, Tibshirani R, Friedman J, Franklin J. The elements of statistical learning: Data mining, inference and prediction. The Mathematical Intelligencer. 2005;**27**(2):83-85 [71] Breiman L. Arcing classifier (with discussion and a rejoinder by the author). The Annals

[73] Mazaheri H, Ghaedi M, Azqhandi MA, Asfaram A. Application of machine/statistical learning, artificial intelligence and statistical experimental design for the modeling and optimization of methylene blue and Cd (ii) removal from a binary aqueous solution by natural walnut carbon. Physical Chemistry Chemical Physics. 2017;**19**(18):11299-11317

[74] Tanzifi M, Yaraki MT, Kiadehi AD, Hosseini SH, Olazar M, Bhati AK, Agarwal S, Gupta VK, Kazemi A. Adsorption of Amido black 10B from aqueous solution using polyani-

nanocomposite: Experimental investigation and artificial neural network mod-

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Austria: R Foundation for Statistical Computing Vienna; 2012

[72] Breiman L. Random forests. Machine Learning. 2001;**45**(1):5-32

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Analyst. 2010;**135**(2):230-267

398 Artificial Intelligence - Emerging Trends and Applications

Statistics. 2001:1189-1232

of Statistics. 1998;**26**(3):801-849

line/SiO<sup>2</sup>

A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields.

**Keywords:** chemical engineering, AI algorithms, classification, process control, modeling, optimization, fault detection and diagnosis

## **1. Introduction**

Artificial intelligence (AI) applications in chemical engineering have increased dramatically recently. This chapter deals with various applications of artificial intelligence (AI) in the chemical engineering field including process such as modeling, optimization, process control, fault detection and diagnosis. The aim of the chapter is to provide an overview of the field by presenting the capabilities and limitations of using the AI approach, focusing on artificial neural network (ANN) and fuzzy logic methods.

It is shown that complexities of conventional approaches when dealing with chemical processes which are inherently highly nonlinear can be tackled through the application of AI methods. Four illustrative relevant examples are also presented.

After reading this chapter, the reader is expected to have a basic grounding in the application of AI methods in chemical engineering and understand their implementation issues.

## **2. Application of AI in chemical process modeling**

Chemical process models which present the system behavior are useful in all phases of chemical engineering, from research and design to optimization and control and even plant operations [1].

Generally, There are two major types of modeling approaches in chemical engineering, namely, mechanistic (white box, first principle) and AI-based approach like ANN and fuzzy logic methods. In the mechanistic approach, fundamental physical and chemical laws, such as conservation laws, construct the basis of the model. This approach contains algebraic and differential equations which involve mass, energy and momentum balances. Due to the large number of variables affecting the process behavior and complex mathematical equations governing the system, many chemical processes are nonlinear and complicated. Consequently, it is hard and sometimes even impossible to present them by mechanistic models. Even if such a model has been developed, it might be impractical to solve or identify its parameters. Moreover, a mechanistic model needs detailed knowledge and a lot of skill and ingenuity to incorporate the basic phenomena of the process in the model. Difficulties can arise from poor knowledge [2]. In some cases, considering some assumptions such as physical properties' constancy, ideality of gas phase and linearization of the nonlinear equations of the model is inevitable, which all impose limitations on the model leading to the reduction of the model's robustness [3].

Among the types of ANN structures, the multi-layer perceptron (MLP) neural network which has a feed-forward scheme is believed as the most useful topology for system modeling [8]. Moreover, the recurrent ANN model which is a mapping of past inputs and outputs to the

Application of AI in Chemical Engineering http://dx.doi.org/10.5772/intechopen.76027 401

In the fuzzy model approach where the two types of which are Mamdani [9] and Takagi-Sugeno (TS) [10], all the uncertainties and model complications are treated in linguistic expressions in the form of "If-Then" rules based on the theory of fuzzy logic [11]. TS like ANN

Mamdani Fuzzy that differs in the way the information and rules are presented has several superiorities over the TS approach for the modeling of chemical processes. First, qualitative experience and knowledge of the experts who are dealing with the process are incorporated in the development of the model [12]. In addition, there is no need for data in order to build the Mamdani fuzzy model. Consequently, a Mamdani fuzzy model is more intuitive, transparent and interpretable [13]. In contrast, each TS-type model is a local approximator and the predictability of the model is valid for the specific operating condition of the process under which the model was developed and tested [14]. Accordingly, it can hardly be applied for analyzing the process behavior and cannot be scaled up or down and therefore is less useful for industrial practice. Despite the capabilities of Mamdani method, it is worth underlying that a Mamdani fuzzy model suffers from the large number of rules when dealing with the

Genetic algorithm (GA) can be used to optimize the performance of a fuzzy model. The role of GA is recognized as optimal parameters' estimation such as the parameters of scaling functions and the universes of discourse [15, 16] or the membership functions (MFs) [17, 18]. GA is also applied as a method for rule reduction/selection by removing some rules like redundant, unnecessary or misleading ones [17] when dealing with high-dimensional problems in which

is commonly considered as a data-driven AI-based modeling approach [12].

the number of rules is so large that it cannot be managed efficiently.

future outputs can be used for dynamic processes.

**Figure 1.** Schematic of AI model.

processes with large number of variables.

On the contrary, AI-based techniques have demonstrated their superb ability and have received much attention for chemical process modeling. These techniques, for which developing detailed knowledge of the process is of less concern, may overcome the drawbacks of the mechanistic approach when dealing with complex and nonlinear systems. Using AI-based methods, inherently qualitative variables in chemical processes like catalyst deactivation in reactors can also be considered in the model, while these types of variables are not possible to implement in mechanistic models.

The most common methods of AI for modeling purposes in chemical engineering are ANN and fuzzy logic, which sometimes are hybridized with evolutionary algorithms [4–7]. In addition to ANN and fuzzy logic methods, their hybrid scheme named adaptive-network-based fuzzy inference system (ANFIS) which is actually a fuzzy inference system implemented in the framework of adaptive networks has also been applied for modeling purposes in chemical engineering.

The first step of developing an AI-based model is defining the input/output variables of the system which is to be modeled (**Figure 1**). Afterward, according to the experimental data or the knowledge of the governing phenomena, the model is developed. The parameters that characterize the AI-based model like the number of fuzzy sets (when using fuzzy logic), the number and the transfer functions of hidden layers (when using the ANN method) depend on the complexity and nonlinearity of the system and the types of variables affecting the process.

**Figure 1.** Schematic of AI model.

After reading this chapter, the reader is expected to have a basic grounding in the application

Chemical process models which present the system behavior are useful in all phases of chemical engineering, from research and design to optimization and control and even plant opera-

Generally, There are two major types of modeling approaches in chemical engineering, namely, mechanistic (white box, first principle) and AI-based approach like ANN and fuzzy logic methods. In the mechanistic approach, fundamental physical and chemical laws, such as conservation laws, construct the basis of the model. This approach contains algebraic and differential equations which involve mass, energy and momentum balances. Due to the large number of variables affecting the process behavior and complex mathematical equations governing the system, many chemical processes are nonlinear and complicated. Consequently, it is hard and sometimes even impossible to present them by mechanistic models. Even if such a model has been developed, it might be impractical to solve or identify its parameters. Moreover, a mechanistic model needs detailed knowledge and a lot of skill and ingenuity to incorporate the basic phenomena of the process in the model. Difficulties can arise from poor knowledge [2]. In some cases, considering some assumptions such as physical properties' constancy, ideality of gas phase and linearization of the nonlinear equations of the model is inevitable, which all

impose limitations on the model leading to the reduction of the model's robustness [3].

On the contrary, AI-based techniques have demonstrated their superb ability and have received much attention for chemical process modeling. These techniques, for which developing detailed knowledge of the process is of less concern, may overcome the drawbacks of the mechanistic approach when dealing with complex and nonlinear systems. Using AI-based methods, inherently qualitative variables in chemical processes like catalyst deactivation in reactors can also be considered in the model, while these types of variables are not possible to

The most common methods of AI for modeling purposes in chemical engineering are ANN and fuzzy logic, which sometimes are hybridized with evolutionary algorithms [4–7]. In addition to ANN and fuzzy logic methods, their hybrid scheme named adaptive-network-based fuzzy inference system (ANFIS) which is actually a fuzzy inference system implemented in the framework of adaptive networks has also been applied for modeling purposes in chemical

The first step of developing an AI-based model is defining the input/output variables of the system which is to be modeled (**Figure 1**). Afterward, according to the experimental data or the knowledge of the governing phenomena, the model is developed. The parameters that characterize the AI-based model like the number of fuzzy sets (when using fuzzy logic), the number and the transfer functions of hidden layers (when using the ANN method) depend on the complexity and nonlinearity of the system and the types of variables affecting the process.

of AI methods in chemical engineering and understand their implementation issues.

**2. Application of AI in chemical process modeling**

400 Artificial Intelligence - Emerging Trends and Applications

tions [1].

implement in mechanistic models.

engineering.

Among the types of ANN structures, the multi-layer perceptron (MLP) neural network which has a feed-forward scheme is believed as the most useful topology for system modeling [8]. Moreover, the recurrent ANN model which is a mapping of past inputs and outputs to the future outputs can be used for dynamic processes.

In the fuzzy model approach where the two types of which are Mamdani [9] and Takagi-Sugeno (TS) [10], all the uncertainties and model complications are treated in linguistic expressions in the form of "If-Then" rules based on the theory of fuzzy logic [11]. TS like ANN is commonly considered as a data-driven AI-based modeling approach [12].

Mamdani Fuzzy that differs in the way the information and rules are presented has several superiorities over the TS approach for the modeling of chemical processes. First, qualitative experience and knowledge of the experts who are dealing with the process are incorporated in the development of the model [12]. In addition, there is no need for data in order to build the Mamdani fuzzy model. Consequently, a Mamdani fuzzy model is more intuitive, transparent and interpretable [13]. In contrast, each TS-type model is a local approximator and the predictability of the model is valid for the specific operating condition of the process under which the model was developed and tested [14]. Accordingly, it can hardly be applied for analyzing the process behavior and cannot be scaled up or down and therefore is less useful for industrial practice. Despite the capabilities of Mamdani method, it is worth underlying that a Mamdani fuzzy model suffers from the large number of rules when dealing with the processes with large number of variables.

Genetic algorithm (GA) can be used to optimize the performance of a fuzzy model. The role of GA is recognized as optimal parameters' estimation such as the parameters of scaling functions and the universes of discourse [15, 16] or the membership functions (MFs) [17, 18]. GA is also applied as a method for rule reduction/selection by removing some rules like redundant, unnecessary or misleading ones [17] when dealing with high-dimensional problems in which the number of rules is so large that it cannot be managed efficiently.

**4. Application of neural networks in chemical process control**

can be effectively controlled for a number of complex and nonlinear processes [23].

*<sup>M</sup>*(*k*) <sup>=</sup> <sup>∅</sup> (*ysp*, *<sup>y</sup>*(*<sup>k</sup>* <sup>−</sup> <sup>1</sup>), …,*y*(*<sup>k</sup>* <sup>−</sup> *<sup>n</sup>*), *<sup>M</sup>*(*<sup>k</sup>* <sup>−</sup> <sup>1</sup> <sup>−</sup> *<sup>τ</sup>*), …,*M*(*<sup>k</sup>* <sup>−</sup> *<sup>m</sup>* <sup>−</sup> *<sup>τ</sup>*)

values of the process model state variables and the past control action.

and separation and purification [26, 27].

the input and output as shown below:

The process control strategies have been developed to improve the performance of the process, reduce energy consumption and ensure high safety and environmental goals. The conventional controllers cannot show satisfactory responses in many industrial chemical processes with high nonlinear dynamics and parameter uncertainties, whereas AI approaches

Because of their high potential for handling nonlinear relationships and self-learning capabilities, there has been considerable interest in the use of neural networks for the control in different fields of chemical processes such as thermal processes [24], reaction processes [25]

One of the algorithms based on neural network control is the inverse model control. In this approach, it is assumed that the input vector for neural network is the required future or reference output together with the past inputs and the past output variables; the approach can help to make for better performance the controlled variables when the unmeasured disturbance is present. The manipulated variable of the controlled plant is the output of the neural network controller [23]. In the system with time delay (τ), if the orders of dynamic model for the output and the input are *n* and *m*, the inverse model can be expressed as the function of

where ∅ represents the function of the inverse model, *k* is the discrete time and *M*, *y* and *ysp* are the control action, the output of the plant and the set point controller, respectively. Therefore, the controller predicts the control action, as shown in **Figure 3**, by having current and past

Fuzzy systems have been used in different applications for controlling chemical processes [28–30]. Researchers also used fuzzy logic controller coupled with an optimal control in an exothermic chemical reaction [31], a batch polymerization reactor [32] and polymerization processes [33]. In addition, since time delay can be often seen in many industrial chemical processes, a possible alternative is the fuzzy model predictive control (FMPC) which has been proposed [34, 35]. In systems with uncertainties of the system model, the choice of type-1 fuzzy may not always be the appropriate solution for a control problem [36]. In these cases, the type-2 fuzzy logic control has been represented in many fields of chemical processes [37, 38]. Hybrid controller based on AI strategies combines two or more AI techniques in order to improve control performance of the chemical process. One of the most popular strategies is the adaptive neuro-fuzzy inference system (ANFIS) controller. This approach is a hybrid intelligent system which uses the learning ability of the neural network with the knowledge

representation of the fuzzy logic [39]. The schematic of ANFIS model with two inputs (x<sup>1</sup>

tains five layers of feed-forward neural network which are explained as follows:

and one output (φ) is shown in **Figure 4**. As shown in **Figure 4**, the ANFIS architecture con-

) (1)

Application of AI in Chemical Engineering http://dx.doi.org/10.5772/intechopen.76027 403

 and x<sup>2</sup> )

**Figure 2.** Schematic of hybrid Mamdani fuzzy and GA modeling method.

The hybrid Mamdani fuzzy and GA modeling methods commonly consist of two main steps: (i) constructing a start-up version of the model using only the heuristic knowledge and (ii) tuning procedure using the GA. The schematic of this algorithm is shown in **Figure 2**. In the first step, the output variables determining the behavior of the system are defined, given that, the input variables which affect the selected output variables are determined. Afterward, a base fuzzy model is defined, characterized by the number and types of fuzzy sets of variables and the production rules presenting the behavior of the process based on the knowledge and expertise of the experts who have been working with the system. This model is used as the start-up version of the model which has to be tuned. In the second step, GA is formulated for optimization of parameters that characterize model, such as membership function parameters, membership function types and so on.

## **3. Application of AI in optimization of chemical processes**

Chemical process optimization has its origins in linear programming at the beginning of the 1960s [19]. This problem is finding the best solution from a variety of efficient alternatives of design or operating variables in order to minimize or maximize a desired objective function. In a general way, the objective function can be the minimization of the operating costs and the undesired material production or the maximization of energy efficiency, the yields and operation productivity, the profitability, safety and the reliability of the plant.

Most chemical processes are nonlinear and complex, so there are many solutions (in some cases becoming endless) in the optimization problems. Such problems are often too complex to be solved through gradient-based optimization approaches. Evolutionary algorithms (EAs) like GA [20], harmony search [21], particle swarm optimization [22] and so on categorized in the AI-based method that is a generic population-based metaheuristic optimization algorithm are capable of efficiently finding an optimal solution in complex problems, such as optimization of chemical processes.

## **4. Application of neural networks in chemical process control**

The process control strategies have been developed to improve the performance of the process, reduce energy consumption and ensure high safety and environmental goals. The conventional controllers cannot show satisfactory responses in many industrial chemical processes with high nonlinear dynamics and parameter uncertainties, whereas AI approaches can be effectively controlled for a number of complex and nonlinear processes [23].

Because of their high potential for handling nonlinear relationships and self-learning capabilities, there has been considerable interest in the use of neural networks for the control in different fields of chemical processes such as thermal processes [24], reaction processes [25] and separation and purification [26, 27].

One of the algorithms based on neural network control is the inverse model control. In this approach, it is assumed that the input vector for neural network is the required future or reference output together with the past inputs and the past output variables; the approach can help to make for better performance the controlled variables when the unmeasured disturbance is present. The manipulated variable of the controlled plant is the output of the neural network controller [23]. In the system with time delay (τ), if the orders of dynamic model for the output and the input are *n* and *m*, the inverse model can be expressed as the function of the input and output as shown below:

The hybrid Mamdani fuzzy and GA modeling methods commonly consist of two main steps: (i) constructing a start-up version of the model using only the heuristic knowledge and (ii) tuning procedure using the GA. The schematic of this algorithm is shown in **Figure 2**. In the first step, the output variables determining the behavior of the system are defined, given that, the input variables which affect the selected output variables are determined. Afterward, a base fuzzy model is defined, characterized by the number and types of fuzzy sets of variables and the production rules presenting the behavior of the process based on the knowledge and expertise of the experts who have been working with the system. This model is used as the start-up version of the model which has to be tuned. In the second step, GA is formulated for optimization of parameters that characterize model, such as membership function param-

Chemical process optimization has its origins in linear programming at the beginning of the 1960s [19]. This problem is finding the best solution from a variety of efficient alternatives of design or operating variables in order to minimize or maximize a desired objective function. In a general way, the objective function can be the minimization of the operating costs and the undesired material production or the maximization of energy efficiency, the yields and

Most chemical processes are nonlinear and complex, so there are many solutions (in some cases becoming endless) in the optimization problems. Such problems are often too complex to be solved through gradient-based optimization approaches. Evolutionary algorithms (EAs) like GA [20], harmony search [21], particle swarm optimization [22] and so on categorized in the AI-based method that is a generic population-based metaheuristic optimization algorithm are capable of efficiently finding an optimal solution in complex problems, such as optimiza-

eters, membership function types and so on.

**Figure 2.** Schematic of hybrid Mamdani fuzzy and GA modeling method.

402 Artificial Intelligence - Emerging Trends and Applications

tion of chemical processes.

**3. Application of AI in optimization of chemical processes**

operation productivity, the profitability, safety and the reliability of the plant.

$$M(k) = \mathcal{Q}\left(y\_{\nu^{\prime}}y(k-1), \dots, y(k-n), M(k-1-\tau), \dots, M(k-m-\tau)\right) \tag{1}$$

where ∅ represents the function of the inverse model, *k* is the discrete time and *M*, *y* and *ysp* are the control action, the output of the plant and the set point controller, respectively. Therefore, the controller predicts the control action, as shown in **Figure 3**, by having current and past values of the process model state variables and the past control action.

Fuzzy systems have been used in different applications for controlling chemical processes [28–30]. Researchers also used fuzzy logic controller coupled with an optimal control in an exothermic chemical reaction [31], a batch polymerization reactor [32] and polymerization processes [33]. In addition, since time delay can be often seen in many industrial chemical processes, a possible alternative is the fuzzy model predictive control (FMPC) which has been proposed [34, 35]. In systems with uncertainties of the system model, the choice of type-1 fuzzy may not always be the appropriate solution for a control problem [36]. In these cases, the type-2 fuzzy logic control has been represented in many fields of chemical processes [37, 38].

Hybrid controller based on AI strategies combines two or more AI techniques in order to improve control performance of the chemical process. One of the most popular strategies is the adaptive neuro-fuzzy inference system (ANFIS) controller. This approach is a hybrid intelligent system which uses the learning ability of the neural network with the knowledge representation of the fuzzy logic [39]. The schematic of ANFIS model with two inputs (x<sup>1</sup> and x<sup>2</sup> ) and one output (φ) is shown in **Figure 4**. As shown in **Figure 4**, the ANFIS architecture contains five layers of feed-forward neural network which are explained as follows:

**4.1. First layer**

change between 0 and 1.

**4.2. Second layer**

strength of a rule.

**4.3. Third layer**

**4.4. Fourth layer**

**4.5. Fifth layer**

This layer is named as an input layer. Each neuron in this layer saves the parameters of the membership function and crisp inputs are converted to membership degree values which

Application of AI in Chemical Engineering http://dx.doi.org/10.5772/intechopen.76027 405

Each neuron of this layer performs a connective operation (i.e., "AND") to calculate the firing

The normalized firing strength is multiplied by a linear combination of the inputs (i.e., Takagi-

The application of ANFIS in the process control of chemical plants was seen in the distillation

A fault is defined as a deviation from an acceptable range of an observable variable or calculated parameter that is referred to as a failure. A failure can be described as diversity of malfunctions in the real plant which can be caused due to instruments' failures, disturbances and plant parameter uncertainties. The abnormal conditions in a plant can result in financial losses. Therefore, in the chemical processes, fault detection and diagnosis have been the focal point of many researches and various fault detection and diagnosis strategies have been presented in the literature. The fault diagnostic systems should possess desirable characteristics

One of the intelligent fault diagnosing techniques is neural network systems. Because of their high potential for capturing nonlinear relationships, neural networks represent a powerful tool for fault diagnosis [43–47]. In fault detection based on neural networks, the number of neurons in the input and output layers are equal to the number of measured variables and the number of potential faults in the process, respectively. The outputs of the neural diagnoser

The last layer of the network is the weighted average of the outputs of the fourth layer.

**5. Application of AI techniques in fault detection and diagnosis of**

such as quick detection, isolability, robustness and multiple fault identifiability [42].

A normalization process is performed by the neurons of this layer.

Sugeno fuzzy rule) in order to obtain a rules layer.

column [40], biodiesel reactor [41].

**chemical engineering**

**Figure 3.** Schematic of neural network inverse model control.

**Figure 4.** Schematic architecture of ANFIS model with two fuzzy rules for two inputs and one output.

#### **4.1. First layer**

This layer is named as an input layer. Each neuron in this layer saves the parameters of the membership function and crisp inputs are converted to membership degree values which change between 0 and 1.

#### **4.2. Second layer**

Each neuron of this layer performs a connective operation (i.e., "AND") to calculate the firing strength of a rule.

#### **4.3. Third layer**

A normalization process is performed by the neurons of this layer.

#### **4.4. Fourth layer**

The normalized firing strength is multiplied by a linear combination of the inputs (i.e., Takagi-Sugeno fuzzy rule) in order to obtain a rules layer.

#### **4.5. Fifth layer**

**Figure 4.** Schematic architecture of ANFIS model with two fuzzy rules for two inputs and one output.

**Figure 3.** Schematic of neural network inverse model control.

404 Artificial Intelligence - Emerging Trends and Applications

The last layer of the network is the weighted average of the outputs of the fourth layer.

The application of ANFIS in the process control of chemical plants was seen in the distillation column [40], biodiesel reactor [41].

## **5. Application of AI techniques in fault detection and diagnosis of chemical engineering**

A fault is defined as a deviation from an acceptable range of an observable variable or calculated parameter that is referred to as a failure. A failure can be described as diversity of malfunctions in the real plant which can be caused due to instruments' failures, disturbances and plant parameter uncertainties. The abnormal conditions in a plant can result in financial losses. Therefore, in the chemical processes, fault detection and diagnosis have been the focal point of many researches and various fault detection and diagnosis strategies have been presented in the literature. The fault diagnostic systems should possess desirable characteristics such as quick detection, isolability, robustness and multiple fault identifiability [42].

One of the intelligent fault diagnosing techniques is neural network systems. Because of their high potential for capturing nonlinear relationships, neural networks represent a powerful tool for fault diagnosis [43–47]. In fault detection based on neural networks, the number of neurons in the input and output layers are equal to the number of measured variables and the number of potential faults in the process, respectively. The outputs of the neural diagnoser are binary variables representing the occurrence of a fault (if the corresponding value is 1) or the lack of fault occurrence (if the corresponding value is 0) [47].Another fault diagnosis approach of AI techniques is fuzzy logic, which is applied in chemical processes [48–51]. In fault diagnosis based on fuzzy logic, the fuzzy relations between faults and symptoms are assumed to be from one to many (i.e., one fault may cause several symptoms). For example:

Illustration case study one: Prediction of virus removal from water using microfiltration

One of the separation technologies for virus removal from water for municipal effluent reuse is the application of membranes. Conventional modeling approaches for predicting membrane performance suffer from various limitations such as the lack of predictive fouling models or complexities of estimating the properties of the membrane surface and membrane interactions. In this case study, an optimum Mamdani fuzzy model is developed for removal prediction of two types of viruses from water [2]. The GA is employed for optimal estimation of parameters characterizing the membership functions of the input/output variables of the model. The first step is defining input/output variables of the model.The amount of virus rejection (R%) which is considered as an output variable of the model is determined as

*Cp* \_\_\_

are virus concentrations in permeate and feed, respectively. The input vari-

ables of the model are concentration of FMD virus (CFMD), concentration of IBR virus (CIBR), operating pressure (P), volume (V) and rpm (stirring speed). The experimental data can found in the works of Madaeni and Kurdian [2]. All the variables that exist in the system are dis-

The next steps are setting a fuzzy inference system using initial fuzzy sets with parameters and defining the fitness function. Two parameters of the Gaussian membership function

 denote the vector of fuzzy model and data set, respectively. There are two methods for the genetically tuning procedure. For cases with a low number of rules, the parameters of membership functions in all rules (for both input and output variables) can be considered as decision variables through optimization formulation. In this way, a variable at each rule can have different optimized shape membership functions. This approach increases the prediction capability yet at the cost of reducing the interpretability of the model. The second method is used when there are a large number of rules in the model. In this method, each variable of the model in all rules has the same optimized shape of the membership function. This method has a lower number of decision variables in optimization

\_\_\_ *<sup>x</sup>* <sup>−</sup> *<sup>x</sup>*¯ *<sup>σ</sup>* ) 2

*Cf*) (2)

Application of AI in Chemical Engineering http://dx.doi.org/10.5772/intechopen.76027 407

) (3)

<sup>2</sup> (4)

membrane based on hybrid Mamdani fuzzy and GA.

*<sup>R</sup>* % <sup>=</sup> <sup>100</sup>(<sup>1</sup> <sup>−</sup>

cretized by Gaussian-type membership functions.

including *x*¯ and *σ*(Eq. (3)) are obtained via GA.

*f*(*x*) = exp(−(

*MSE* = (*ym* − *ye*)

The mean square error (MSE) is selected as the fitness function as follows:

formulation for the same case when compared with the first method.

follows:

where Cp and Cf

where ym and ye

```
If Sym1
        is S1,n AND Sym2
                          is S2,n AND … AND Symm is Sm,n Then F1
                                                                    is Hn
```
where *Symi* (*i* = 1… *m*) is the vector of fuzzy input variables (symptoms) and *Fi* (*j* = 1 … *n*) is the fuzzy output variables (Faults). *Si, j* is the input linguistic value relevant to jth output and *Hj* is the output linguistic value. The schematic of a fuzzy fault detection system is shown in **Figure 5** [51].