AI-driven sustainable environmental foundation research system
In recent years,complex environmental problems have become increasingly common,and traditional research methods struggle to respond flexibly and swiftly to new environmental challenges.The rapid development of artificial intelligence(AI)technology offers fresh perspectives for sustainable environmental foundation research.This paper first introduces key information approximation methods and system equations for complex dynamic environmental substance systems,based on which a machine learning framework for steady-state and dynamic environmental substance systems is proposed.The paper delves into the application of artificial intelligence in fundamental scientific fields such as molecule property prediction,material design,and protein synthesis,and further discusses its transfer applications in environmental research,including new pollutants prediction,environmental functional material design,and active sludge microbial community optimization.Based on this,the paper details the application of artificial intelligence in water treatment process optimization and mechanism research,demonstrating its potential in solving environmental issues,and proposes a multi-component information fusion strategy for environmental systems.Nonetheless,realizing the widespread application of AI in the environmental field requires overcoming challenges related to the construction of essential descriptions of the system,data scarcity,and the optimization of multi-component information fusion techniques.
artificial intelligence(AI)environmental system engineeringenvironmental applicationswater treatmentphysics-informed model
万方数据
维普
