A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence

Chemosphere
Mingyi FanXionghui Wei

Abstract

Water pollution occurs mainly due to inorganic and organic pollutants, such as nutrients, heavy metals and persistent organic pollutants. For the modeling and optimization of pollutants removal, artificial intelligence (AI) has been used as a major tool in the experimental design that can generate the optimal operational variables, since AI has recently gained a tremendous advance. The present review describes the fundamentals, advantages and limitations of AI tools. Artificial neural networks (ANNs) are the AI tools frequently adopted to predict the pollutants removal processes because of their capabilities of self-learning and self-adapting, while genetic algorithm (GA) and particle swarm optimization (PSO) are also useful AI methodologies in efficient search for the global optima. This article summarizes the modeling and optimization of pollutants removal processes in water treatment by using multilayer perception, fuzzy neural, radial basis function and self-organizing map networks. Furthermore, the results conclude that the hybrid models of ANNs with GA and PSO can be successfully applied in water treatment with satisfactory accuracies. Finally, the limitations of current AI tools and their new developments are also highli...Continue Reading

Citations

Jun 27, 2019·Computational Intelligence and Neuroscience·Fatin Aqilah Binti Abdul AzizJarinah Mohd Ali
Nov 26, 2020·Scientific Reports·Hamid GholamiAdrian L Collins
Feb 22, 2021·Environmental Science and Pollution Research International·Suraj Kumar BhagatZaher Mundher Yaseen
Jan 10, 2020·The Science of the Total Environment·Zhiping YeJiade Wang
Jul 18, 2021·Medical & Biological Engineering & Computing·Farideh MohammadiZeynab Yavari
Aug 25, 2021·Chemosphere·Anjali A Meshram, Sharad M Sontakke
Oct 6, 2021·Environmental Science and Pollution Research International·Waidah IsmailMohd Zamani Zulkifli

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