A dual approach to optimization in wastewater treatment plants: Deterministic and stochastic perspectives
Lizeth Pichardo-Zárate, Alicia Román‐Martínez · Journal of environmental chemical engineering · 2025
Efficient design of wastewater treatment plants (WWTPs), particularly in terms of biological process configuration and operational cost optimization under variable conditions, is critical for sustainable performance. This study compares a deterministic optimization approach, which assumes fixed input conditions, with a stochastic framework that incorporates uncertainty through Monte Carlo simulations and metaheuristic algorithms—Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). A municipal WWTP was modeled under three configurations (Conventional, Ludzack-Ettinger, and Bardenpho) to evaluate operational costs and effluent quality indicators, including COD (≤90 mg/L), BOD (≤30 mg/L), and NH₃-N (≤15 mg/L), under uncertainty. Stochastic optimization achieved up to 68.5 % cost reduction and ensured effluent compliance, with PSO emerging as the most cost-effective method. Sensitivity analysis revealed hydraulic retention time and dissolved oxygen as the most influential variables affecting performance. Beyond cost savings, the novelty of this work lies in integrating uncertainty analysis with metaheuristic optimization across multiple plant configurations, providing a robust, scenario-based decision-making tool for real-world WWTP management.