The monitoring and operation processes of a water network have traditionally been carried out using sensors and SCADAs, but they are largely operator-dependent. Given the criticality and the volumes of data handled, infrastructure managers are now running advanced algorithms at the edge. In this article, we explore the real case of deploying Machine Learning at the Edge to optimise chemical control processes in real time.
To operate more efficiently, water management operators face the challenge to monitor in a unified and comprehensive way all components that make up the OT and industrial automation architectures. The use of Edge Nodes allows connectivity with different equipment in each installation regardless of the various data reporting equipment. Discover how utility companies can access sophisticated intelligence to support water operators using Edge Computing.
Although much of the water infrastructures in place are dozens of years old, in recent times their processes have changed radically thanks to a large extent, to digital technology. Edge computing is one of those technologies that will change the shape of the water industry due to the speed and reliability it provides when modernizing this type of infrastructure.
How can the water industry benefit from IoT Edge and AI? IoT is one of the technologies that has made a strong entry into the water industry, developing solutions rich in monitoring, analytical, and automation features. Meanwhile, Artificial intelligence (AI) is making its mark on the water industry, powering intelligent operations using machine learning to optimize resource use and operational budgets for organizations.
When we talk about "water sustainability", experts use some fundamental indicators. For us, the most important is the resilience of their infrastructures, i.e. how prepared they are for adverse situations, and the efficiency of their processes, measured as the number of resources to be invested in meeting a specific demand. AI plays a fundamental role in these processes.