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Open AccessArticle
Evaluating Municipal Solid Waste Incineration Through Determining Flame Combustion to Improve Combustion Processes for Environmental Sanitation
by
Jian Tang
Jian Tang 1,2
,
Xiaoxian Yang
Xiaoxian Yang 1,2,
Wei Wang
Wei Wang 3,*
and
Jian Rong
Jian Rong 3
1
School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
2
Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China
3
College of Information Engineering, Dalian Ocean University, Dalian 116023, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(19), 8872; https://doi.org/10.3390/su17198872 (registering DOI)
Submission received: 18 July 2025
/
Revised: 30 September 2025
/
Accepted: 30 September 2025
/
Published: 4 October 2025
Abstract
Municipal solid waste (MSW) refers to solid and semi-solid waste generated during human production and daily activities. The process of incinerating such waste, known as municipal solid waste incineration (MSWI), serves as a critical method for reducing waste volume and recovering resources. Automatic online recognition of flame combustion status during MSWI is a key technical approach to ensuring system stability, addressing issues such as high pollution emissions, severe equipment wear, and low operational efficiency. However, when manually selecting optimized features and hyperparameters based on empirical experience, the MSWI flame combustion state recognition model suffers from high time consumption, strong dependency on expertise, and difficulty in adaptively obtaining optimal solutions. To address these challenges, this article proposes a method for constructing a flame combustion state recognition model optimized based on reinforcement learning (RL), long short-term memory (LSTM), and parallel differential evolution (PDE) algorithms, achieving collaborative optimization of deep features and model hyperparameters. First, the feature selection and hyperparameter optimization problem of the ViT-IDFC combustion state recognition model is transformed into an encoding design and optimization problem for the PDE algorithm. Then, the mutation and selection factors of the PDE algorithm are used as modeling inputs for LSTM, which predicts the optimal hyperparameters based on PDE outputs. Next, during the PDE-based optimization of the ViT-IDFC model, a policy gradient reinforcement learning method is applied to determine the parameters of the LSTM model. Finally, the optimized combustion state recognition model is obtained by identifying the feature selection parameters and hyperparameters of the ViT-IDFC model. Test results based on an industrial image dataset demonstrate that the proposed optimization algorithm improves the recognition performance of both left and right grate recognition models, with the left grate achieving a 0.51% increase in recognition accuracy and the right grate a 0.74% increase.
Share and Cite
MDPI and ACS Style
Tang, J.; Yang, X.; Wang, W.; Rong, J.
Evaluating Municipal Solid Waste Incineration Through Determining Flame Combustion to Improve Combustion Processes for Environmental Sanitation. Sustainability 2025, 17, 8872.
https://doi.org/10.3390/su17198872
AMA Style
Tang J, Yang X, Wang W, Rong J.
Evaluating Municipal Solid Waste Incineration Through Determining Flame Combustion to Improve Combustion Processes for Environmental Sanitation. Sustainability. 2025; 17(19):8872.
https://doi.org/10.3390/su17198872
Chicago/Turabian Style
Tang, Jian, Xiaoxian Yang, Wei Wang, and Jian Rong.
2025. "Evaluating Municipal Solid Waste Incineration Through Determining Flame Combustion to Improve Combustion Processes for Environmental Sanitation" Sustainability 17, no. 19: 8872.
https://doi.org/10.3390/su17198872
APA Style
Tang, J., Yang, X., Wang, W., & Rong, J.
(2025). Evaluating Municipal Solid Waste Incineration Through Determining Flame Combustion to Improve Combustion Processes for Environmental Sanitation. Sustainability, 17(19), 8872.
https://doi.org/10.3390/su17198872
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