Research Projects

Wastewater Treatment Plants (WWTPs) face challenges in efficiency due to changes in influent wastewater and environmental protection requirements. Existing data-driven solutions include supervised learning methods like partial least squares, support vector machines, relevant vector machines, and Gaussian process regression. However, these methods struggle to measure essential quality-related variables like BOD5, COD, SVI, and Total Nitrogen. Multi-output regression models, such as multivariable partial least squares, multivariable vector machine, and multivariable Gaussian regression approaches, can be improved with adaptive learning techniques. Deep learning technology has rapidly evolved and has been used in various fields, including wastewater treatment plants. This project aims to address these issues by collecting municipal wastewater data from different WWTPs in India and Canada. The research deliverables include developing machine learning approaches to predict effluent quality, adaptive frameworks for prediction of effluent quality, GHG emissions, and nutrient recovery, reinforcement learning methods at process supervisory and regulatory layers for control of DO and Nitrate, and experimental validation on a pilot-scale WWTP.