Most water utilities have to handle a substantial number of customer complaints every year. Traditionally, complaints are handled by skilled staff who know how to identify primary issues, classify complaints, find solutions, and communicate with customers. The effort associated with complaint processing is often great, depending on the number of customers served by a water utility. However, the rise of natural language processing (NLP), enabled by deep learning, and especially the use of deep recurrent and convolutional neural networks, has created new opportunities for comprehending and interpreting text complaints. As such, we aim to investigate the value of the use of NLP for processing customer complaints. Through a case study about the Water Utility Groningen in the Netherlands, we demonstrate that NLP can parse language structures and extract intents and sentiments from customer complaints. As a result, this study represents a critical and fundamental step toward fully automating consumer complaint processing for water utilities.

In this paper a scenario-based robust optimization approach is proposed to take demand uncertainty into account in the design of water distribution networks. This results in insight in the trade-off between costs and performance of different designs. Within the proposed approach the designer is able to choose the desired degree of risk aversion, and the performance of the design can be assessed based on the water demand effectively supplied under different scenarios. Both future water demand scenarios and scenarios based on historical records are considered. The approach is applied to the design of a real-life water distribution network supplying part of a city in the Netherlands. From the results the relation between costs and performance for different scenarios becomes evident: a more robust design requires higher design costs. Moreover, it is proven that numerical optimization helps finding better design solutions when compared to manual approaches. The developed approach allows water utilities to make informed choices about how much to invest in their infrastructure and how to design it in order to achieve a certain level of robustness.

Numerical optimization is gradually finding its way into drinking water practice. For successful introduction of optimization into the sector, it is important that researchers and utility experts work together on the problem formulation with the water utility experts. Water utilities heed the solutions provided by optimization techniques only when the underlying approach and performance criteria match their specific goals. In this contribution, we demonstrate the application of numerical optimization on a real-life problem. The Belgian utility De Watergroep is looking to not only reinforce its distribution networks but to also structurally modify the network’s topology to enhance the quality of water delivered in the future. To help the utility explore the possibilities of these far-reaching changes in the most flexible way possible, an optimization problem was formulated to optimize topology and pipe sizing simultaneously for the distribution network of a Belgian city. The objective of the problem is to minimize the volume of the looped network and thereby work towards a situation where most of the customers are fed by branched extremities of the network. This objective is constrained by pressure and fire flow requirements and thresholds on the number of customers on the branched sections. The requirements for continuity of supply under failure scenarios are guaranteed by these constraints, as verified in the final solution. The results of the optimization process show that it is possible to design a network which is 18.5% cheaper than the currently existing network. Moreover, it turns out the—previously completely meshed—topology can be restructured so that 67% of the network length is turned into branched clusters, with a meshed superstructure of 33% of the length remaining.

Despite global efforts to reduce non-revenue water (NRW), average levels remain at 30% worldwide and 25% in European countries. This paper investigates whether these persistent water losses stem primarily from uncontrollable environmental factors or from management practices. Through two rounds of questionnaires with water utilities across multiple countries and exploratory data analysis of data on 120 utilities, the research examines geographical conditions, utility characteristics, and water loss reduction strategies. Findings reveal that geographical conditions significantly impact Infrastructure Leakage Index (ILI) levels across Europe, suggesting that these factors should be considered when setting reduction targets. Also, a clear progression exists from low-resource measures implemented by high-ILI utilities to sophisticated technologies adopted by low-ILI utilities. While underinvestment likely contributes to high water losses, limited network investment data prevented a comprehensive analysis. The study recommends that the EU drinking water directive should account for utility size, establish measurement standards, encourage partnerships between large and small utilities, and include smaller utilities in reporting requirements. Additionally, incorporating economic metrics and investment data for pipe rehabilitation and digital technologies would enhance understanding of effective water loss reduction strategies. Comprehensive, standardized data collection could catalyze innovations to reduce ‘unavoidable’ losses and significantly decrease overall water losses across Europe.

This paper presents a practical application of a sensitivity matrix-based methodology for determining optimal pressure sensor locations for leak detection in a water distribution network (WDN). The optimization is formulated as multi-objective, exploring the tradeoff between the minimization of the number of sensors to be installed and the maximization of the detection coverage. The methodology is applied to a real-life WDN serving the area of Seppe in the Netherlands. Close collaboration with the water utility enabled us to explore alternative designs, fine-tune the optimization problem and obtain results that are ready for practical implementation.

Water distribution networks are expected to fulfill the water demand by all consumers and at all times, even during critical scenarios, such as pipe failures. In this work, a methodology is proposed to maximize the quality of service during pipe failures by operating valves. The selection of the valves to operate is done by solving an optimization problem using Gondwana, a generic optimization tool for drinking water distribution networks. Different objective functions and different failure scenarios are investigated, considering a real-life water distribution network. The analysis is performed considering the peak demand condition. The proposed methodology is useful for water companies in managing the operation of their networks during critical scenarios.