Optimization Of Wastewater Treatment Plant Processes With The Help Of AI

As energy costs rise and climate change drives more severe weather, wastewater treatment plants must balance sustainability with efficiency. Stadtwerke Trier (SWT), operator of Germany’s oldest city’s largest WWTP, faced this challenge. Despite investments in energy-efficient technology, the plant remained a major energy consumer and needed intelligent optimization to reach energy self-sufficiency. Partnering with Aquatune GmbH, now part of Xylem, SWT implemented Xylem Vue’s Plant Real-Time Decision Support application. This AI-powered system, built on artificial neural networks and real-time SCADA data, created a digital twin of the plant. By simulating hundreds of scenarios in seconds, it determined the most efficient aeration and chemical dosing strategies while maintaining effluent compliance. The solution reduced aeration energy use by over 20%, saving 200,000 kWh annually, while ensuring reliable operations. This case highlights how data-driven modeling and predictive analytics can transform wastewater treatment from energy-intensive to energy-smart operations.