Enhanced Sludge Dewatering Efficiency through Adaptive Polymer Dosage Optimization Using a Multi-Modal Predictive Model

**Abstract:** Traditional sludge dewatering processes in centrifugal decanters (CDs) are often hampered by inefficient polymer dosage strategies, leading to suboptimal cake solids and increased operational costs. This paper proposes a novel, data-driven approach to adaptive polymer dosage optimization by integrating real-time measurements of sludge characteristics, operational parameters, and historical performance data within a multi-modal predictive model. Leveraging a combination of process analytical technology (PAT), machine learning, and advanced control algorithms, our system dynamically adjusts polymer dosage to maximize cake solids while minimizing polymer consumption. We demonstrate the feasibility and efficacy of this approach through simulations and pilot-scale experiments, highlighting its potential for significant improvements in dewatering efficiency and cost-effectiveness in wastewater treatment plants. This framework exhibits immediate commercial readiness and is readily deployable in existing CD systems with minimal infrastructure modifications.