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

• Published in: Sustainability Vol. 17; no. 7; p. 3048
• Main Authors: Yang, Weiwei, Tang, Jian, Tian, Hao, Wang, Tianzheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2025
• Subjects: Algorithms; Artificial intelligence; Carbon; Combustion research; Consumption; Decision trees; Desalination; Efficiency; Flue gas; Flue gases; Fuzzy logic; Gases; Incineration; Industrial plant emissions; Mathematical optimization; Methods; Multilevel analysis; Municipal solid waste; Neural networks; Optimization; Oxygen; Pollutants; Sustainable development; China
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su17073048

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 R2 increases by 140.8%, compared to the BO-EnTSFRT method that uses only the main model.