A Hybrid Data-Driven Intensified Mechanistic Modeling Framework for Accurate and Reliable Wastewater Treatment Process Simulation and Control

values for sulfide, sulfur, and sulfate increasing from 0.30, 0.09, and 0.02 to 0.66, 0.77, and 0.74, respectively. Predictive robustness was also enhanced, with extreme deviations reduced by 78.2% relative to the data-driven model. Using HyDIM, a bi-objective optimization strategy achieved stable long-term performance, with 96% sulfide removal and 91% sulfur recovery, a 41% improvement in sulfur recovery over the mechanistic strategy. Overall, this work presents a novel modeling paradigm for reliable decision support and control in WWT.