Memetic Optimization of Wastewater Pumping Systems for Energy Efficiency: AI Optimization in a Simulation-Based Framework for Sustainable Operations Management

Agostino G. Bruzzone (), Marco Gotelli, Marina Massei, Xhulia Sina, Antonio Giovannetti, Filippo Ghisi and Luca Cirillo
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Agostino G. Bruzzone: Simulation Team, Via Magliotto 2, 17100 Savona, Italy
Marco Gotelli: Simulation Team, Via Magliotto 2, 17100 Savona, Italy
Marina Massei: Simulation Team, Via Magliotto 2, 17100 Savona, Italy
Xhulia Sina: Simulation Team, Via Magliotto 2, 17100 Savona, Italy
Antonio Giovannetti: Simulation Team, Via Magliotto 2, 17100 Savona, Italy
Filippo Ghisi: Simulation Team, Via Magliotto 2, 17100 Savona, Italy
Luca Cirillo: Simulation Team, Via Magliotto 2, 17100 Savona, Italy

Sustainability, 2025, vol. 17, issue 14, 1-21

Abstract: This study investigates the integration of advanced optimization algorithms within energy-intensive infrastructures and industrial plants. In fact, the authors focus on the dynamic interplay between computational intelligence and operational efficiency in wastewater treatment plants (WWTPs). In this context, energy optimization is thought of as a hybrid process that emerges at the intersection of engineered systems, environmental dynamics, and operational constraints. Despite the known energy-intensive nature of WWTPs, where pumps and blowers consume over 60% of total power, current methods lack systematic, real-time adaptability under variable conditions. To address this gap, the study proposes a computational framework that combines hydraulic simulation, manufacturer-based performance mapping, and a Memetic Algorithm (MA) capable of real-time optimization. The methodology synthesizes dynamic flow allocation, auto-tuning mutation, and step-by-step improvement search into a cohesive simulation environment, applied to a representative parallel-pump system. The MA’s dual capacity to explore global configurations and refine local adjustments reflects both static and kinetic aspects of optimization: the former grounded in physical system constraints, the latter shaped by fluctuating operational demands. Experimental results across several stochastic scenarios demonstrate consistent power savings (12.13%) over conventional control strategies. By bridging simulation modeling with optimization under uncertainty, this study contributes to sustainable operations management, offering a replicable, data-driven tool for advancing energy efficiency in infrastructure systems.

Keywords: Sustainable operations management; Memetic Algorithm; Water Treatment Optimization; energy efficiency; artificial intelligence; Modeling and Simulation; Sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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