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

被引：0

作者：

Heo, Sungku ^{[1
,2
]}

Oh, Taeseok ^{[3
]}

Woo, Taeyong ^{[1
]}

Kim, Sangyoon ^{[1
]}

Choi, Yunkyu ^{[3
]}

Park, Minseok ^{[3
]}

Kim, Jeonghoon ^{[3
]}

Yoo, Changkyoo ^{[1
,4
]}

机构：

[1] Kyung Hee Univ, Coll Engn, Dept Environm Sci & Engn, Integrated Engn, 1732 Deogyeong Daero, Yongin 17104, Gyeonggi, South Korea

[2] Imperial Coll London, Dept Civil & Environm Engn, London SW7 2AZ, England

[3] BKT Co Ltd, 25 Yuseong Daero 1184 Beon Gil, Daejeon 34109, South Korea

[4] Kyung Hee Univ, Coll Engn, Dept Environm Sci & Engn, Seoul 446701, Gyeonggi Do, South Korea

来源：

DESALINATION | 2025年 / 593卷

基金：

新加坡国家研究基金会;

关键词：

AI application; Control; Digital twin; Optimization; Wastewater; BIOLOGICAL NITROGEN REMOVAL; WASTE-WATER; START-UP; ANAMMOX; REACTOR; MODEL; TIME;

D O I：

10.1016/j.desal.2024.118235

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

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.

引用

页数：21