Application of machine learning for modelling water quality in distribution systems

Delivery of safe drinking water is an essential public service, and distribution systems are a critical control point for the protection of public health. Distribution systems encounter issues such as aging infrastructure leading to leaks and breaks, pressure management problems, and maintaining water quality during transit to consumers. These problems collectively impact the safety, efficiency, and sustainability of water supply, necessitating continuous advancements in technology, infrastructure investment, and regulatory frameworks to ensure reliable and clean water access. This project is a collaboration between UBCO, KWR, and TUDelft that aims to improve water system management through the development of a digital twin and use of data-driven approaches. Furthermore, the project will investigate the ability to predict and model biofilm growth and conditions within the distribution system. The proposed project contributes to furthering the understanding of potential water quality and maintenance issues that may impact consumers. The research objectives of the participating research groups have significant overlap. However, there are distinct and complementary expertise and approaches to water system modelling.

Faculty Supervisor:

Nicolás Peleato

Student:

Partner:

Delft University of Technology

Discipline:

Engineering

Sector:

Education

University:

The University of British Columbia - Okanagan

Program: