Acidic Gas Prediction Modeling Based on Shared Features and Inverted Transformer of Municipal Solid Waste Incineration Processes

Zenan Li, Wei Wang (), Jian Tang, Yicong Wu and Jian Rong
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Zenan Li: 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
Yicong Wu: College of Information Engineering, Dalian Ocean University, Dalian 116023, China
Jian Rong: College of Information Engineering, Dalian Ocean University, Dalian 116023, China

Sustainability, 2025, vol. 17, issue 21, 1-41

Abstract: Effective management of municipal solid waste is crucial for achieving sustainable development and maintaining a healthy ecological environment. Municipal solid waste incineration (MSWI) processes are highly nonlinear and exhibit strong coupling characteristics, which makes long-term stable control challenging. Accurate prediction of the various toxic and harmful acidic gases that will be generated during this process is crucial for supporting optimization and control research. This study proposes a predictive model for acidic gases using Random Forest (RF) and Inverted Transformer (ITransformer). First, the RF algorithm is used to identify feature variables that strongly correlate with the target variables, thereby facilitating the shared feature selection process for multiple acidic gases. These selected features are then fed into a multi-output ITransformer model, which predicts the target variables and generates multiple evaluation metrics. Finally, the model’s hyperparameters are optimized based on these metrics and the threshold ranges of the acidic gases. The experimental results using real data from a specific incineration plant show that 13 features remain after the shared feature selection process. Compared to other models, the proposed approach uses the fewest shared features while reducing computational costs. Moreover, the R 2 values for NO x , SO 2 , and HCl are 0.9791, 0.9793, and 0.9838, respectively.

Keywords: municipal solid waste incineration; acid gas prediction; multi-output evaluation metrics; shared feature selection (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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