Artificial intelligence for management and control of pollution minimization and mitigation processes

Abstract

The reduction of environmental pollution and the conservation and recycling of natural resources are significant social and environmental concerns. As valuable means for pollution control, minimization and mitigation remain attractive approaches. However, interactive, dynamic and uncertain features are associated with these processes, resulting in difficulties in their management and control. Artificial intelligence (AI) is an effective approach for tackling these complexities. In this study, the recent advancements of AI-based technologies for management and control of pollution minimization and mitigation processes are examined. Literature relevant to the area of application of AI to control and management of pollution minimization and mitigation processes is investigated. Especially, technologies of expert systems, fuzzy logic, and neural networks, which emerge as the most frequently employed approaches for realizing process control, are highlighted. The results not only provide an overview of the updated progress in the study field but also, more importantly, reveal perspectives of research for more effective environmental process control through the AI-aided measures. Several demanding areas for enhanced research efforts are discussed, including issues of data availability and reliability, methodology validity, and system complexity.