Artificial intelligence-implemented prediction and cost-effective optimization of micropollutant photodegradation using g-C3N4/Bi2O3 heterojunction

Micropollutants pose a formidable hazard to human health, however, conventional water treatment fails to efficiently and economically eliminate them. The imperative need for real-time concentration monitoring of these micropollutants, vital for effective elimination and cost optimization, has proven elusive without the intervention of artificial intelligence. To bridge these critical gaps, g-C3N4/Bi2O3 heterojunction was proposed as an efficient catalyst to eliminate recalcitrate micropollutants under visible light in this study, demonstrating a 1.8-fold increase in catalytic efficiency compared to g-C3N4, and furthermore, artificial intelligence models, i.e., the proposed Long short-term memory combined with Gated residual network (ALG) and Deep Deterministic Policy Gradient (DDPG), were employed for real-time concentration prediction of micropollutants (R-2 = 0.9541) and their treatment processing optimization, respectively. The predominant active species for degrading micropollutants were identified as O-2(center dot-) and O-1(2). We proved that HOO center dot serves as an intermediate to transform HO center dot and H2O2 generated in-situ to O-2(center dot-) and O-1(2), further enhancing micropollutant elimination. Compared to other models, the ALG demonstrated significantly lower root mean square error (RMSE = 0.0432) and mean absolute percentage error (MAPE = 4.8273). Weight analysis revealed that Catalyst Type (31.80 %) is the most critical factor. Finally, the proposed DDPG model dynamically adjusted its treatment strategy, aiming to discover an optimal balance among cost, reaction time, and removal efficiency. This study provides mechanistic insights into the pivotal roles of artificial intelligence in prediction and cost-effective optimization of micropollutant elimination.