Tackling environmental challenges in pollution controls using artificial intelligence: A review

Ye, Zhiping, Yang, Jiaqian, Zhong, Na, Tu, Xin ORCID: 0000-0002-6376-0897, Jia, Jining and Wang, Jiade
(2020) Tackling environmental challenges in pollution controls using artificial intelligence: A review. SCIENCE OF THE TOTAL ENVIRONMENT, 699. 134279-.

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This review presents the developments in artificial intelligence technologies for environmental pollution controls. A number of AI approaches, which start with the reliable mapping of nonlinear behavior between inputs and outputs in chemical and biological processes in terms of prediction models to the emerging optimization and control algorithms that study the pollutants removal processes and intelligent control systems, have been developed for environmental clean-ups. The characteristics, advantages and limitations of AI methods, including single and hybrid AI methods, were overviewed. Hybrid AI methods exhibited synergistic effects, but with computational heaviness. The up-to-date review summarizes i) Various artificial neural networks employed in wastewater degradation process for the prediction of removal efficiency of pollutants and the search of optimizing experimental conditions; ii) Evaluation of fuzzy logic used for intelligent control of aerobic stage of wastewater treatment process; iii) AI-aided soft-sensors for precisely on-line/off-line estimation of hard-to-measure parameters in wastewater treatment plants; iv) Single and hybrid AI methods applied to estimate pollutants concentrations and design monitoring and early-warning systems for both aquatic and atmospheric environments; v) AI modelings of short-term, mid-term and long-term solid waste generations, and various ANNs for solid waste recycling and reduction. Finally, the future challenges of AI-based models employed in the environmental fields are discussed and proposed.

Item Type: Article
Uncontrolled Keywords: Artificial neural network, Environmental pollutants, Intelligent control, Soft measurement, Early-warning
Depositing User: Symplectic Admin
Date Deposited: 09 Sep 2019 07:56
Last Modified: 19 Jan 2023 00:27
DOI: 10.1016/j.scitotenv.2019.134279
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3053764