College of Environment, Zhejiang University of Technology, Hangzhou 310014, PR China.
Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, United Kingdom.
Sci Total Environ. 2020 Jan 10;699:134279. doi: 10.1016/j.scitotenv.2019.134279. Epub 2019 Sep 5.
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.
这篇综述介绍了人工智能技术在环境污染控制方面的发展。已经开发了许多人工智能方法，从可靠地映射化学和生物过程中输入和输出之间的非线性行为开始，到新兴的优化和控制算法，这些算法研究污染物去除过程和智能控制系统。综述了 AI 方法的特点、优点和局限性，包括单一和混合 AI 方法。混合 AI 方法表现出协同效应，但计算量较大。最新的综述总结了：i）各种人工神经网络在废水降解过程中用于预测污染物去除效率和寻找优化实验条件；ii）用于智能控制好氧阶段废水处理过程的模糊逻辑的评估；iii）用于精确在线/离线估计废水处理厂中难以测量参数的人工智能辅助软传感器；iv）用于估计水和大气环境中污染物浓度和设计监测与预警系统的单一和混合 AI 方法；v）用于短期、中期和长期固体废物产生的 AI 建模，以及各种用于固体废物回收和减少的神经网络。最后，讨论并提出了用于环境领域的基于人工智能的模型未来面临的挑战。
