MODEL PREDICTIVE CONTROL FOR ACCURATE DOSING IN WASTEWATER TREATMENT PLANTS BASED ON SELF-ATTENTION MECHANISM

PEI Lifeng; CHEN Weijie; XU Jingsheng; LÜ Lu

doi:10.13205/j.hjgc.202311015

Volume 41 Issue 11

Nov. 2023

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PEI Lifeng, CHEN Weijie, XU Jingsheng, LÜ Lu. MODEL PREDICTIVE CONTROL FOR ACCURATE DOSING IN WASTEWATER TREATMENT PLANTS BASED ON SELF-ATTENTION MECHANISM[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(11): 84-92,140. doi: 10.13205/j.hjgc.202311015

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MODEL PREDICTIVE CONTROL FOR ACCURATE DOSING IN WASTEWATER TREATMENT PLANTS BASED ON SELF-ATTENTION MECHANISM

doi: 10.13205/j.hjgc.202311015

1. State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210046, China;
2. Joint Institute of International Environmental Technology Innovation of Changzhou Hi-Tech District and Nanjing University, Changzhou 213125, China

Received Date: 2022-12-08
Available Online: 2023-12-25

Abstract

Abstract

Precise control is an effective way to solve the problems of high operating costs and substandard effluent quality of wastewater treatment plants, but due to the non-linear, time-varying, and time-lagging nature of the sewage treatment process, conventional control methods can hardly meet the demand. This study proposed a model predictive control method to achieve accurate dosing of chemicals in sewage treatment plants. The constructed model was based on the self-attention mechanism to extract valid information from the input sequence and to model the complex non-linear relationships between features, thus improving the prediction accuracy. Data from a denitrification filter at a wastewater treatment plant in Jiangsu Province were used for training and testing. Compared with CNN and LSTM, the results showed that the MER and MSE metrics of the self-attention model is the best for the prediction of effluent TN, COD and NH₄⁺-N, achieving more accurate prediction. The predictive control model using a particle swarm optimization algorithm to calculate the dosage was applied to this wastewater treatment plant. The results showed that the effluent achieved more than 95% compliance for TN and the optimized dosage was 28.72% and 21.78% lower than the monthly average dosage of the sewage treatment plant respectively.
- deep learning,
- model predictive control,
- interpretable machine learning,
- self-attention mechanism,
- particle swarm optimization,
- accurate dosing

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References(21)

References

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MODEL PREDICTIVE CONTROL FOR ACCURATE DOSING IN WASTEWATER TREATMENT PLANTS BASED ON SELF-ATTENTION MECHANISM

doi: 10.13205/j.hjgc.202311015

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MODEL PREDICTIVE CONTROL FOR ACCURATE DOSING IN WASTEWATER TREATMENT PLANTS BASED ON SELF-ATTENTION MECHANISM

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