Towards the integrated operation of advanced technologies in the Figueres Drinking Water Treatment Plant

Drinking water treatment plants play a key role in ensuring that the water supplied to the population is safe and of good quality. This task is becoming increasingly challenging due to changing environmental conditions, climate variability, growing human pressures on water resources, and stricter regulations, particularly regarding by-products formed during disinfection. To respond to these challenges, treatment plants need to continuously adapt their operation and base their decisions on reliable and timely information. In this context, this doctoral research focuses on the development of tools to support better decision-making in drinking water treatment. The work was carried out as part of an industrial PhD in collaboration with Figueres de Serveis S.A. (Catalonia, Spain), using the Figueres drinking water treatment plant as a case study. The research combines laboratory experiments, data analysis, and full-scale plant application to address both chemical and microbiological risks in drinking water production. The main outcome of the thesis is the development and full-scale validation of an intelligent system for adjusting ozone dosage in a flexible and adaptive way. The system uses real-time measurements, such as UV absorbance at 254 nm and water temperature, to adapt the oxidation process to changes in natural organic matter. Results show that this approach helps maintain good treatment performance, reduce chemical consumption, and avoid the excessive formation of biodegradable organic compounds that may promote bacterial growth. In addition to this validated system, the thesis proposes two further control modules. One is aimed at assessing chemical risks by estimating the potential formation of disinfection by-products, while the other focuses on microbiological risks using indicators related to biodegradable organic matter and bacterial regrowth. Together, these elements form the basis of a future decision support framework that would allow a more integrated and risk-informed management of drinking water treatment processes. Overall, this thesis helps connect scientific knowledge with the day-to-day operation of drinking water treatment plants. The results provide a solid foundation for moving towards more predictive, adaptive, and resilient water management systems, with the ultimate goal of ensuring safe, sustainable, and regulation-compliant drinking water in the long term.