Yingzheng Fan

Abstract The water treatment industry urgently requires innovative materials to address persistent and emerging contaminants. However, conventional materials discovery processes remain slow and largely serendipitous, hindered by the ‘dark matter’ within complex purification mechanisms and stringent electronic structure requirements.

Abstract Click to copy section linkSection link copied! Intelligent control and optimization by reinforcement learning (RL) agents have emerged as a promising framework for biological nutrient removal (BNR) processes. However, most existing studies remain confined to simulation environments, limiting their credibility and engineering relevance. In addition, the black-box nature of RL agents hinders operator trust.