Real-scale demonstration of digital twins-based aeration control policy optimization in partial nitritation/Anammox process: Policy iterative dynamic programming approach

Partial nitritation (PN) and anaerobic ammonium oxidation (Anammox) process is a promising energy-efficient nitrogen removal method in wastewater sector. Recently, artificial intelligence (AI)-driven process operation techniques are widely researched. However, there is few research to demonstrate AI application into a full-scale wastewater treatment plant (WWTP) due to operational complexity of WWTP. This study conducts a real-scale demonstration of digital twin-based aeration control policy (DT-O₂CTRL) to autonomously control the full-scale PN/A process under high nitrogen influent loads. For this, chemical oxygen demand (COD) and NH₄-N in influent and reactors, were collected through the online sensors. Then, digital twin (DT) model of full-scale PN/A process was mathematically developed. Finally, policy iterative dynamic programming (PIDP), inspired from the reinforcement learning, was suggested as the core algorithm of AI-O₂CTRL to maintain a NO₂-N/NH₄-N ratio (NNR) which is a critical operation factor in PN/A process. The results showed that the DT model showed an accuracy of >95 %. Based on the DT model, the AI-O₂CTRL algorithm autonomously controls the NNR at the target value of 1.1 and reduces electricity consumption by 16.7 % when treating around 400 m³/d of enriching nitrogen loads. Finally, it can reduce the operational cost by 19,724.01$/year regardless of the influent load fluctuations.