Optimization of wastewater treatment plant operation using artificial intelligence and machine learning

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Modern wastewater treatment plants are fitted with SCADA systems that continually collect a significant amount of data. This proposed project is intended to use that data and develop artificial intelligence/machine learning (AI/ML) based models which can then be used to predict and automatically improve the operation of biological treatment at wastewater treatment plants (WWTP). The project will build on existing research and develop AI/ML models using data from a specific wastewater treatment plant in Calgary. The plant data will be used as a case study and the success of this research will lead to the development of AI/ML driven dynamic control tools which will make WWTP operating parameters adapt to the changing influent water characteristics and optimize WWTP performance. These machine-driven dynamic control tools will lead to substantial energy savings for WWTP, reduce operational cost and minimize environmental impacts of municipal wastewater by improving effluent quality and lowing greenhouse gas emissions.