Multi-Point Serial Temperature Prediction Modeling in the Combustion and Heat Exchange Stages of Municipal Solid Waste Incineration

Yongqi Zhang, Wei Wang (), Jian Tang and Jian Rong
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Yongqi Zhang: College of Information Engineering, Dalian Ocean University, Dalian 116023, China
Wei Wang: College of Information Engineering, Dalian Ocean University, Dalian 116023, China
Jian Tang: School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
Jian Rong: College of Information Engineering, Dalian Ocean University, Dalian 116023, China

Sustainability, 2025, vol. 17, issue 16, 1-36

Abstract: Accurate temperature control across different zones during the combustion and heat exchange stages is crucial for both the economic efficiency of municipal solid waste incineration (MSWI) power plants and the consistent achievement of environmental targets. To address limitations in existing research, such as single-point temperature prediction models and the difficulty in characterizing the correlation mapping between adjacent zones, this article proposes a multi-point serial temperature prediction modeling method for the combustion and heat exchange stages of the MSWI process. Firstly, based on identifying five key temperature points across different zones in these stages, the Pearson correlation coefficient (PCC) is utilized for regional feature selection targeting each individual temperature point. Subsequently, multiple single temperature point prediction models based on a linear regression decision tree (LRDT) are constructed using the selected feature variables. Finally, considering the mutual influence between temperatures in neighboring zones, a serial multi-point temperature prediction model is built by using the knowledge transfer. To our knowledge, this is the first interpretable multi-point temperature prediction model for the MSWI process. It can assist in precise temperature control across different zones during the combustion and heat exchange stages in future studies. Validation results demonstrate that the minimum MSE attained 0.0238, the minimum MAE reached 0.1223, and the maximum R 2 achieved 0.9985 across multiple temperature points. The proposed method is validated using actual operational data from an MSWI power plant in Beijing.

Keywords: municipal solid waste incineration (MSWI); combustion and heat exchange stages; regional feature selection; multi-point serial temperature prediction; linear regression decision tree (LRDT) (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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