A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence. (June 2018)

• Record Type: Journal Article
• Authors: Fan, Mingyi; Hu, Jiwei; Cao, Rensheng; Ruan, Wenqian; Wei, Xionghui
• Is Part Of: Chemosphere. Volume 200(2018)
• Page Start: 330
• Page End: 343
• Publication Date: 2018-06
• Subjects: Water treatment -- Environmental pollutants -- Experimental design -- Artificial intelligence -- Artificial neural networks -- Genetic algorithm; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2018.02.111
• Languages: English
• ISSNs: 0045-6535

Abstract

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 highlighted for prospective applications in the environmental protection. Highlights: Fundamentals, advantages and limitations were discussed for ANNs, GA and PSO. Studies were summarized on modeling of removal processes using ANNs, GA and PSO. Predicting performances for removal processes were compared between different ANNs. Developments of AI tools were described for the optimization of removal processes.