Flue Gas Oxygen Content Model Based on Bayesian Optimization Main–Compensation Ensemble Algorithm in Municipal Solid Waste Incineration Process

Weiwei Yang, Jian Tang (), Hao Tian and Tianzheng Wang
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Weiwei Yang: School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
Jian Tang: School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
Hao Tian: School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
Tianzheng Wang: School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China

Sustainability, 2025, vol. 17, issue 7, 1-43

Abstract: The municipal solid waste incineration (MSWI) process plays a crucial role in managing the risks associated with waste accumulation and promoting the sustainable development of urban environments. However, unstable operation of the MSWI process can lead to excessive pollutant emissions, deteriorating air quality, and adverse impacts on public health. Flue gas oxygen content is a key controlled variable in the MSWI process, and its stable control is closely linked to both incineration efficiency and pollutant emissions. Developing a high-precision, interpretable model for flue gas oxygen content is essential for achieving optimal control. However, existing methods face challenges such as poor interpretability, low accuracy, and the complexity of manual hyperparameter tuning. To address these issues, this article proposes a flue gas oxygen content model based on a Bayesian optimization (BO) main–compensation ensemble modeling algorithm. The model first utilizes an ensemble TS fuzzy regression tree (EnTSFRT) to construct the main model. Then, a long short-term memory network (LSTM) is employed to build the compensation model, using the error of the EnTSFRT model as the target value. The final output is obtained through a weighted combination of the main and compensation models. Finally, the hyperparameters of the main–compensation ensemble model are optimized using the BO algorithm to achieve a high generalization performance. Experimental results based on real MSWI process data demonstrate that the proposed method performs well, achieving a 48.2% reduction in RMSE and a 53.1% reduction in MAE, while R 2 increases by 140.8%, compared to the BO-EnTSFRT method that uses only the main model.

Keywords: municipal solid waste incineration (MSWI); pollution reduction; flue gas oxygen content; main–compensation ensemble model; ensemble TS fuzzy regression tree (EnTSFRT); long short-term memory (LSTM); Bayesian optimization (BO) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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