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Feasibility-guided evolutionary optimization of pump station design and operation in water networks

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I publikationen:Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 34455-34470
Huvudupphov: Faúndez-Lizama, Thalía
Övriga upphov: Gutiérrez-Bahamondes, Jimmy H., Gajardo-Sepúlveda, Nicolás, Mora-Meliá, Daniel
Utgiven:
Nature Publishing Group
Ämnen:
Mathematical models
Algorithms
Optimization techniques
Drainage
Civil engineering
Feasibility
Machine learning
Computer applications
Energy consumption
Water distribution
Learning algorithms
Case studies
Water quality
Scheduling
Simulation
Infrastructure
Costs
Genetic algorithms
Objective function
Water scarcity
Methods
Pumping stations
Hydraulics
Economic
Länkar:Citation/Abstract
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Abstrakt:Pumping stations are critical elements of water distribution networks (WDNs), as they ensure the required pressure for supply but represent the highest energy consumption within these systems. In response to increasing water scarcity and the demand for more efficient operations, this study proposes a novel methodology to optimize both the design and operation of pumping stations. The approach combines Feasibility-Guided Evolutionary Algorithms (FGEAs) with a Feasibility Predictor Model (FPM), a machine learning-based classifier designed to identify feasible solutions and filter out infeasible ones before performing hydraulic simulations. This significantly reduces the computational burden. The methodology is validated through a real-scale case study using four FGEAs, each incorporating a different classification algorithm: Extreme Gradient Boosting, Random Forest, K-Nearest Neighbors, and Decision Tree. Results show that the number of objective function evaluations was reduced from 50,000 to fewer than 25,000. Additionally, The FGEAs based on Extreme Gradient Boosting and Random Forest outperformed the original algorithm in terms of objective value. These results confirm the effectiveness of integrating machine learning into evolutionary optimization for solving complex engineering problems and highlight the potential of this methodology to reduce operational costs while improving computational efficiency in WDNs.
ISSN:2045-2322
DOI:10.1038/s41598-025-17630-w
Källa:Science Database