Machine learning-integrated multi-objective application and optimization framework for sulfur-based reactive filler...

Sulfur-siderite composite reactive fillers (SSCReFs) hold strong commercial and engineering potential for nutrient-rich wastewater treatment, yet their performance is intrinsically governed by nonlinear coupling among sulfur-driven denitrification, proton-mediated siderite dissolution, and irreversible material consumption. These interactions disrupt proportional relationships between filler composition, influent chemistry, and nutrient removal, rendering empirical tuning fundamentally unreliable. To overcome this limitation, we developed an interpretable, machine learning (ML)-integrated framework (SSCReF-MAOF) that captures nonlinear sulfur-iron-microbe-mineral interactions and enables multi-objective optimization of effluent quality, consumption-guided compositing (CGC), and treatment cost. Experiments showed that higher sulfur-to-siderite ratios or elevated alkalinity enhance denitrification, whereas siderite-rich formulations promote dephosphorization and strengthen CGC under alkalinity-limited conditions. SSCReF-MAOF integrates five optimized ML models with high predictive accuracy (R² = 0.930-0.981) and identifies mechanistically meaningful features linked to both sulfur oxidation/iron dissociation and effluent quality dynamics. By combining ML predictions with stoichiometric constraints, the framework determines cost-minimized filler formulations that meet regulatory nutrient-removal compliance and material CGC requirements. Model interpretation further highlights the central role of dissociated iron in coordinating nitrogen-phosphorus removal, advancing understanding of sulfur-iron geochemistry in engineered biosystems. Finally, a paired graphical user interface, together with a site-specific case study further demonstrates practical deployability, providing wastewater treatment plants with an intelligent decision-support tool for tailored SSCReF design and advanced nutrient polishing.