However, when you introduce sophisticated tools like machine learning, the digital twin becomes ‘supercharged’ and supports a more seamless route to rapidly building a model with minimal human intervention. With artificial intelligence and computer science, the technology can utilize past data and automatically ‘calibrate’ to better represent infrastructure. As the data updates in real-time, it is immediately fed back into the system for continuous refinement and improvement.
The resulting real-time decision support system (RTDSS) is able to continuously learn and adapt, allowing operators to see what’s happening within their system at any given moment. The RTDSS can then generate multiple scenarios and provide operational recommendations, making it easier for operators to control their assets, processes, or systems. Ultimately, this advanced application of digital twin technology has the potential to deliver autonomous, optimized control.
Digital twins represent the single largest technological breakthrough we’ve seen in wastewater this lifetime
Now, more than ever, treatment plant managers are being asked to make proactive, informed operational decisions. Yet, many don’t have that insight or visibility into their operations previously mentioned, or the support of additional staff or funding. That’s where the digital twin can play a transformative role. The technologies and applications typically used in wastewater treatment have had little advancement over the years, but digital twins represent the single largest technological breakthrough we’ve seen in wastewater this lifetime – it’s really quite amazing.
Take Plant Real-Time Decision Support, for example. The solution leverages real-time data from a sensor network and applies this to process models, coupled with an optimizer, to arrive at operational recommendations and decision support that helps managers run their treatment plants more effectively. Having greater system insight allows them to change certain set points within the system to optimize the treatment process. That can be anything from monitoring energy and chemical usage, to adjusting influent changes and enhancing effluent quality.
Outside of driving operational efficiencies, one of the larger interests in digital twin technology is filling the knowledge gap. Globally, we’re facing a new type of transition where there’s a significant reduction in legacy operators. Ultimately, the goal of a treatment plant manager is to deliver clean water to communities, but that becomes more difficult to maintain as knowledge of operational processes diminish. Digital twin data help new operators pick up where their predecessors left off, so there’s no need to start from scratch when changes in the workforce happen.
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