How much should the water industry trust Artificial Intelligence?
Mar 28 2022
Identification and interpretation of patterns and relationships in the vast amounts of data collected by water utilities has the potential to drive better operating decisions and drive reductions in cost, however, engineers and decision makers are sceptical of using artificial intelligence (AI) or machine learning (ML) as standalone approaches.
Algorithms are often presented and sold as ‘black boxes’ that produce non-transparent, unexplainable outcomes, and require constant oversight and supervision. Large amounts of clean, historical and granular data are needed to be accurate. More importantly, experienced operators and practitioners in the industry see the lack of connection to the industry itself as a weakness. An ML model on its own does not care about the industry that it is applied to and makes no connection to the accepted physics, chemistry and biology of the process. How should the water industry realise the benefits that AI can deliver, while managing the risks that come with the approach?
Digitalisation is driving change in the water and wastewater treatment sectors, just as it is in other industrial and corporate environments. Water and wastewater treatment plants collect more data than ever before. Control and treatment equipment is increasingly augmented with sensors that collect data to support control of the process.