Water systems modelling, data and control

The International Computing & Control for the Water Industry Conference (CCWI) was the 17th in a series founded and organised by the universities of Brunel, De Montfort, Exeter, Imperial College, and Sheffield. Initially run as a biennial series held in the UK, the series has in recent years been held annually and has expanded internationally to Italy, the Netherlands and Canada with Beijing in China hosting the next event. The last time the Centre for Water Systems at the University of Exeter hosted the event was in 2011 and we were proud to host the latest event form 1st to 4th September 2019.

In the early 1990s when CCWI was established, the conference series was well-ahead of its time and only now is the water industry catching up on the immense potential of digitalisation and smart systems. The proliferation of sensors, smart meters, large-scale and widespread data acquisition, data analytics and visualisation, increasingly sophisticated modelling tools, digital twins, and high-speed information and communication technologies will all have profound implications for the management of water systems over the coming years. Existing scientific and engineering knowledge of system modelling, water quality modelling, asset management and performance modelling, and demand, leakage, energy and greenhouse gas management will benefit substantially from advances in sensors, instrumentation and communication technologies, big data, data driven and soft-computing analytics and visualisation. The aim of the conference was therefore to bring professionals researching and working in the field to discuss the emerging ‘WATER 4.0ʹ agenda – water systems modelling, data and control.

This special issue is made up of specially selected, rewritten and reviewed papers from the conference. The papers have been chosen to cover the whole gamut of topics across water systems, modelling and control. They cover the following important topics: state of the art models for Digital Twin-based management of water distribution networks (Conejos Fuertes et al.), asset condition monitoring using deep convolution neural networks (Wu at al.), quality control by inlet reservoir water quality monitoring (Doronina et al.), chlorine wall decay modelling in large-scale systems (Monteiro et al.), discolouration modelling (Al-Saffar and Husband), the dynamics of material detachment under flushing conditions (Sass Braga et al.), leakage management (Rajakumar et al.), and the life cycle assessment of asset management strategies (Hajibabaei et al.).