Optimizing the pump schedule of water distribution systems using a deep learning meta-model

Optimizing the pump schedule of water distribution systems using a deep learning meta-model: To what extent can algorithm unrolling optimize the pump schedule of an urban water distribution system?

Mertzanis, Nick (TU Delft Civil Engineering & Geosciences)

Taormina, R. (mentor)
Schleiss, M.A. (graduation committee)
Garzón Díaz, J.A. (graduation committee)

Delft University of Technology

Water Management

2024-03-15

This thesis investigates the integration of algorithm unrolling and genetic algorithms (GA) for optimizing pump scheduling in water distribution systems (WDS), a critical component for ensuring energy-efficient water delivery. In the context of modern civilization’s reliance on clean, affordable water for diverse uses, the operation of a WDS, particularly through energy-intensive pumps, presents significant challenges. Traditional optimization techniques often resort to hydraulic solvers like EPANET, which, while accurate, are computationally intensive for large-scale applications. Our methodology introduces a meta-model based on algorithm unrolling, building upon prior work and extending it to address pump scheduling with a multi-objective function focusing on both cost and energy efficiency. This approach significantly reduces the computational load, offering a faster alternative to EPANET while maintaining considerable accuracy. The meta-model demonstrated promising results in the Fossolo network, achieving comparable schedules 20 times faster than traditional methods. However, its applicability to more complex networks and its ability to capture detailed system behaviors are limited, highlighting the need for further enhancements in model stability and reproducibility. Despite these limitations, the study emphasizes the potential of meta-models as a complementary tool to traditional methods, especially in scenarios requiring rapid decision-making under computational constraints. This research contributes to the broader field of water utility management, offering insights into more sustainable and efficient operation strategies.

pump scheduling
optimization
meta-model
surrogate model
Deep Learning
Multi-Objective Constrained Optimisation
multi-objective optimization
Water Distribution Network
water distribution system

http://resolver.tudelft.nl/uuid:6427e548-9bc0-4aa6-afb6-a9319ca3693e

Student theses

master thesis

© 2024 Nick Mertzanis