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Shirkoohi MG, Tyagi RD, Vanrolleghem PA, Drogui P. Artificial intelligence techniques in electrochemical processes for water and wastewater treatment: a review. JOURNAL OF ENVIRONMENTAL HEALTH SCIENCE & ENGINEERING 2022; 20:1089-1109. [PMID: 36406623 PMCID: PMC9672199 DOI: 10.1007/s40201-022-00835-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 08/28/2022] [Indexed: 06/16/2023]
Abstract
In recent years, artificial intelligence (AI) techniques have been recognized as powerful techniques. In this work, AI techniques such as artificial neural networks (ANNs), support vector machines (SVM), adaptive neuro-fuzzy inference system (ANFIS), genetic algorithms (GA), and particle swarm optimization (PSO), used in water and wastewater treatment processes, are reviewed. This paper describes applications of the mentioned AI techniques for the modelling and optimization of electrochemical processes for water and wastewater treatment processes. Most research in the mentioned scope of study consists of electrooxidation, electrocoagulation, electro-Fenton, and electrodialysis. Also, ANNs have been the most frequent technique used for modelling and optimization of these processes. It was shown that most of the AI models have been built with a relatively low number of samples (< 150) in data sets. This points out the importance of reliability and robustness of the AI models derived from these techniques. We show how to improve the performance and reduce the uncertainty of these developed black-box data-driven models. From the perspectives of both experiment and theory, this review demonstrates how AI techniques can be effectively adapted to electrochemical processes for water and wastewater treatment to model and optimize these processes. Supplementary Information The online version contains supplementary material available at 10.1007/s40201-022-00835-w.
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Affiliation(s)
- Majid Gholami Shirkoohi
- Institut National de La Recherche Scientifique (INRS), Centre-Eau Terre Environnement, Université du Québec, 490 Rue de la Couronne, Québec, (QC) G1K 9A9 Canada
- CentrEau, Centre de Recherche Sur L’eau, Université Laval, Québec, (QC) Canada
| | | | - Peter A. Vanrolleghem
- CentrEau, Centre de Recherche Sur L’eau, Université Laval, Québec, (QC) Canada
- modelEAU, Département de Génie Civil Et de Génie Des Eaux, Université Laval, 1065 av. de la Médecine, Québec, (QC) G1V 0A6 Canada
| | - Patrick Drogui
- Institut National de La Recherche Scientifique (INRS), Centre-Eau Terre Environnement, Université du Québec, 490 Rue de la Couronne, Québec, (QC) G1K 9A9 Canada
- CentrEau, Centre de Recherche Sur L’eau, Université Laval, Québec, (QC) Canada
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Jiang Y, Zhu X, Xing X. Electrochemical Oxidation of Phenolic Compounds at Boron-Doped Diamond Anodes: Structure–Reactivity Relationships. J Phys Chem A 2017; 121:4326-4333. [DOI: 10.1021/acs.jpca.7b02630] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Yi Jiang
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Department
of Environmental Engineering, Peking University, The Key Laboratory of Water and Sediment Sciences, Ministry of Education, Beijing 100871, China
| | - Xiuping Zhu
- Department
of Civil and Environmental Engineering, Louisiana State University, Baton
Rouge, Louisiana 70803, United States
- Department
of Environmental Engineering, Peking University, The Key Laboratory of Water and Sediment Sciences, Ministry of Education, Beijing 100871, China
| | - Xuan Xing
- College
of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
- Department
of Environmental Engineering, Peking University, The Key Laboratory of Water and Sediment Sciences, Ministry of Education, Beijing 100871, China
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Noorizadeh H, Farmany A. Quantitative structure-electrochemistry relationship for substituted benzenoids using Levenberg-Marquardt artificial neural network. RUSS J ELECTROCHEM+ 2015. [DOI: 10.1134/s102319351503009x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Curteanu S, Godini K, Piuleac CG, Azarian G, Rahmani AR, Butnariu C. Electro-Oxidation Method Applied for Activated Sludge Treatment: Experiment and Simulation Based on Supervised Machine Learning Methods. Ind Eng Chem Res 2014. [DOI: 10.1021/ie500248q] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Silvia Curteanu
- Faculty
of Chemical Engineering and Environmental Protection, Department of
Chemical Engineering, “Gheorghe Asachi” Technical University of Iasi, Bd. Prof. dr. doc. Dimitrie Mangeron, No. 73, 700050, Iasi, Romania
| | - Kazem Godini
- Faculty
of Health, Environmental Health Engineering Department, Ilam University of Medical Sciences, Banganjab Complex, Ilam, Iran
| | - Ciprian G. Piuleac
- Faculty
of Chemical Engineering and Environmental Protection, Department of
Chemical Engineering, “Gheorghe Asachi” Technical University of Iasi, Bd. Prof. dr. doc. Dimitrie Mangeron, No. 73, 700050, Iasi, Romania
| | - Ghasem Azarian
- Faculty
of Health and Research Center for Health Sciences, Department of Environmental
Health Engineering, Hamedan University of Medical Sciences, Hamedan, Iran
| | - Ali R. Rahmani
- Faculty
of Health and Research Center for Health Sciences, Department of Environmental
Health Engineering, Hamedan University of Medical Sciences, Hamedan, Iran
| | - Cristina Butnariu
- Faculty
of Chemical Engineering and Environmental Protection, Department of
Chemical Engineering, “Gheorghe Asachi” Technical University of Iasi, Bd. Prof. dr. doc. Dimitrie Mangeron, No. 73, 700050, Iasi, Romania
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Li B, Dong Y, Ding Z. Heterogeneous fenton degradation of azo dyes catalyzed by modified polyacrylonitrile fiber fe complexes: QSPR (quantitative structure peorperty relationship) study. J Environ Sci (China) 2013; 25:1469-1476. [PMID: 24218861 DOI: 10.1016/s1001-0742(12)60190-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The amidoximated polyacrylonitrile (PAN) fiber Fe complexes were prepared and used as the heterogeneous Fenton catalysts for the degradation of 28 anionic water soluble azo dyes in water under visible irradiation. The multiple linear regression (MLR) method was employed to develop the quantitative structure property relationship (QSPR) model equations for the decoloration and mineralization of azo dyes. Moreover, the predictive ability of the QSPR model equations was assessed using Leave-one-out (LOO) and cross-validation (CV) methods. Additionally, the effect of Fe content of catalyst and the sodium chloride in water on QSPR model equations were also investigated. The results indicated that the heterogeneous photo-Fenton degradation of the azo dyes with different structures was conducted in the presence of the amidoximated PAN fiber Fe complex. The QSPR model equations for the dye decoloration and mineralization were successfully developed using MLR technique. MW/S (molecular weight divided by the number of sulphonate groups) and NN=N (the number of azo linkage) are considered as the most important determining factor for the dye degradation and mineralization, and there is a significant negative correlation between MW/S or NN=N and degradation percentage or total organic carbon (TOC) removal. Moreover, LOO and CV analysis suggested that the obtained QSPR model equations have the better prediction ability. The variation in Fe content of catalyst and the addition of sodium chloride did not alter the nature of the QSPR model equations.
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Affiliation(s)
- Bing Li
- Division of Textile Chemistry & Ecology, School of Textiles, Tianjin Polytechnic University, Tianjin 300387, China.
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Tian M, Thind SS, Simko M, Gao F, Chen A. Quantitative Structure–Reactivity Study of Electrochemical Oxidation of Phenolic Compounds at the SnO2–Based Electrode. J Phys Chem A 2012; 116:2927-34. [DOI: 10.1021/jp3004618] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Min Tian
- Department of Chemistry, Lakehead University, Thunder Bay, Ontario P7B 5E1,
Canada
| | - Sapanbir S. Thind
- Department of Chemistry, Lakehead University, Thunder Bay, Ontario P7B 5E1,
Canada
| | - Matthew Simko
- Department of Chemistry, Lakehead University, Thunder Bay, Ontario P7B 5E1,
Canada
| | - Faming Gao
- Key Laboratory of
Applied Chemistry, Yanshan University,
Qinghuangdao 066004, China
| | - Aicheng Chen
- Department of Chemistry, Lakehead University, Thunder Bay, Ontario P7B 5E1,
Canada
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QSPR study for the prediction of half-wave potentials of benzoxazines by heuristic method and radial basis function neural network. OPEN CHEM 2009. [DOI: 10.2478/s11532-009-0033-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
AbstractThe half-wave potential (E1/2) is an important electrochemical property of organic compounds. In this work, a quantitative structure-property relationship (QSPR) analysis has been conducted on the half-wave reduction potential (E1/2) of 40 substituted benzoxazines by means of both a heuristic method (HM) and a non-linear radial basis function neural network (RBFNN) modeling method. The statistical parameters provided by the HM model (R2 =0.946; F=152.576; RMSCV=0.0141) and the RBFNN model (R2=0.982; F=1034.171 and RMS =0.0209) indicated satisfactory stability and predictive ability. The obtained models showed that benzoxazines with larger Min valency of a S atom (MVSA), lower Relative number of H atom (RNHA) and Min n-n repulsion for a C-H bond (MnnRCHB) and Minimal Electrophilic Reactivity Index for a C atom (MERICA) can be more easily reduced. This QSPR approach can contribute to a better understanding of structural factors of the organic compounds that contribute to the E1/2, and can be useful in predicting the E1/2 of other compounds.
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Leong MK, Chen YM, Chen TH. Prediction of human cytochrome P450 2B6-substrate interactions using hierarchical support vector regression approach. J Comput Chem 2009; 30:1899-909. [DOI: 10.1002/jcc.21190] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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Liu H, Wen Y, Luan F, Gao Y, Li X. Quantitative structure-λmax relationship study on flavones by heuristic method and radial basis function neural network. Anal Chim Acta 2009; 649:52-61. [DOI: 10.1016/j.aca.2009.07.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2009] [Revised: 06/27/2009] [Accepted: 07/03/2009] [Indexed: 11/30/2022]
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Devillers J, Devillers H. Prediction of acute mammalian toxicity from QSARs and interspecies correlations. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2009; 20:467-500. [PMID: 19916110 DOI: 10.1080/10629360903278651] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
With the ever-growing number of xenobiotics that can potentially contaminate the environment, the determination of their mammalian toxicity is of prime importance. In this context, LD50 tests on rats and mice have been used for a long time to express the relative hazard associated with the acute toxicity of inorganic and organic chemicals. However, these laboratory tests encounter important hurdles. They are costly, time consuming and actively opposed by animal rights activists. Moreover, new legislation policies, such as REACH (Registration, Evaluation, Authorization and Restriction of Chemicals), aim at reducing the use of toxicity tests on vertebrates. Consequently, there is a need to find alternative methods for estimating the acute mammalian toxicity of chemicals. The quantitative structure-activity relationships (QSARs) and interspecies correlations appear particularly suited to reaching this goal. In this context, this paper reviews more than 150 models aiming at predicting rat and mouse LD50 values from molecular descriptors or (and) ecotoxicity data. The interest of these computational tools is discussed.
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Hemmateenejad B, Yazdani M. QSPR models for half-wave reduction potential of steroids: A comparative study between feature selection and feature extraction from subsets of or entire set of descriptors. Anal Chim Acta 2009; 634:27-35. [DOI: 10.1016/j.aca.2008.11.062] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2008] [Revised: 11/26/2008] [Accepted: 11/27/2008] [Indexed: 11/25/2022]
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