The high costs of constructing water supply networks have shifted design priorities towards minimizing leakage and enhancing reliability. This study applies nonlinear programming (NLP) to optimize a water distribution network (WDN), focusing on leakage reduction and improved performance. By integrating flow-pressure dynamics, the approach incorporates a reliability constraint within Tehran’s WDN in Iran. High-risk nodes, identified based on leakage potential using WaterGEMS software, serve as key data points for the NLP model. The findings show that NLP significantly improves pressure distribution across the network, contributing to more stable pressure levels, which is critical for operational efficiency. Furthermore, this method enhances the network’s resilience index, effectively reducing the likelihood of leakage and pipe failure. The NLP-optimized network design yields a notable 8.12% reduction in overall pipe costs, indicating both financial and operational advantages of this approach. By addressing these high-risk areas with targeted interventions, the NLP model contributes to a more sustainable network infrastructure that minimizes maintenance needs and extends network lifespan. This comprehensive optimization model thus offers a practical, cost-effective solution for modern water distribution challenges, balancing initial investment with long-term network reliability and leakage control.
Najarchi et al. (Tue,) studied this question.