A master thesis from Aalborg University
Intelligent Control of Wastewater Treatment using Model Predictive Control & Reinforcement Learning
Term
4. term
Education
Publication year
2025
Submitted on
2025-06-13
Pages
92 pages
Abstract
This thesis explores the use of Model Predictive Control (MPC) combined with Reinforcement Learning (RL) to optimize wastewater treatment plant (WWTP) operations, aiming to reduce energy consumption/cost and improve effluent quality. The report proposes a realistic implementation pipeline using a high-fidelity digital twin in WEST (by DHI) and a low-fidelity optimizer in UPPAAL Stratego, connected via the STOMPC MPC interface. Experiments under dry, rain, and storm weather scenarios show that MPC can improve both energy usage and effluent quality simultaneously. For dry weather conditions, MPC reduced energy consumption by up to 24% depending on the scenario.
Keywords
Documents
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