Artificial intelligence techniques in electrochemical processes for water and wastewater treatment: a review

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 optim...

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Bibliographic Details
Published in:Journal of environmental health science and engineering 2022-10, Vol.20 (2), p.1089-1109
Main Authors: Shirkoohi, Majid Gholami, Tyagi, Rajeshwar Dayal, Vanrolleghem, Peter A., Drogui, Patrick
Format: Article
Language:English
Subjects:
Adaptive systems
Analysis
Artificial intelligence
Artificial neural networks
Earth and Environmental Science
Electrochemical reactions
Electrochemistry
Electrocoagulation
Electrodialysis
Environment
Environmental Economics
Environmental Engineering/Biotechnology
Environmental Health
Environmental Law/Policy/Ecojustice
Fuzzy logic
Genetic algorithms
Machine learning
Mathematical optimization
Modelling
Neural networks
Optimization
Particle swarm optimization
Purification
Quality of Life Research
Review
Review Article
Sewage
Support vector machines
Waste Management/Waste Technology
Wastewater treatment
Water treatment
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Summary: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 (
ISSN:2052-336X
2052-336X
DOI:10.1007/s40201-022-00835-w