Optimizing Non-Revenue Water Management: A Review

Abstract

Non-Revenue Water (NRW) refers to the volume of water that is distributed from the water plant but does not get billed to customers, which is a major challenge for water bodies. It represents the difference between the total volume of water pumped into the water distribution system (WDS) and the volume actually billed to customers. NRW is composed of three components: physical losses, commercial losses, and unbilled authorized consumption. These losses cause financial deficits, increased operational costs, and infrastructure deterioration, making NRW reduction a critical challenge for water bodies globally.  This present study reviews optimization strategies for minimizing NRW, focusing on advanced metering infrastructure (AMI), remote leak detection (acoustic, pressure, and flow sensors), geographic information systems (GIS), data analytics, machine learning, and digital twin modeling. Key findings indicate that integrating these technologies can potentially reduce NRW by 15-25% based on case studies from regions employing comprehensive digital solutions. Furthermore, AI-driven predictive maintenance strategies show promise in reducing leak detection times by approximately 30%. Future research should emphasize AI-driven optimization, predictive maintenance, and sustainable water management strategies to optimize NRW, targeting a quantifiable benchmark of reducing overall NRW to below 10% in urban water systems