Tackling environmental challenges in pollution controls using artificial intelligence: A review
Autor: | Jining Jia, Jiade Wang, Zhiping Ye, Yang Jiaqian, Na Zhong, Xin Tu |
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Rok vydání: | 2019 |
Předmět: |
Pollutant
Environmental Engineering Municipal solid waste 010504 meteorology & atmospheric sciences Artificial neural network business.industry Computer science Environmental pollution 010501 environmental sciences 01 natural sciences Pollution Fuzzy logic Wastewater Environmental Chemistry Artificial intelligence business Intelligent control Waste Management and Disposal Predictive modelling 0105 earth and related environmental sciences |
Zdroj: | SCIENCE OF THE TOTAL ENVIRONMENT |
ISSN: | 1879-1026 |
Popis: | 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. |
Databáze: | OpenAIRE |
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