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Online Combustion Status Recognition of Municipal Solid Waste Incineration Process Using DFC Based on Convolutional Multi-Layer Feature Fusion

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  • Xiaotong Pan

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
    Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China)

  • Jian Tang

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
    Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China)

  • Heng Xia

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
    Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China)

  • Tianzheng Wang

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
    Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China)

Abstract

The prevailing method for handling municipal solid waste (MSW) is incineration, a critical process that demands safe, stable, and eco-conscious operation. While grate-typed furnaces offer operational flexibility, they often generate pollution during unstable operating conditions. Moreover, fluctuations in the physical and chemical characteristics of MSW contribute to variable combustion statuses, accelerating internal furnace wear and ash accumulation. Tackling the challenges of pollution, wear, and efficiency in the MSW incineration (MSWI) process necessitates the automatic online recognition of combustion status. This article introduces a novel online recognition method using deep forest classification (DFC) based on convolutional multi-layer feature fusion. The method entails several key steps: initial collection and analysis of flame image modeling data and construction of an offline model utilizing LeNet-5 and DFC. Here, LeNet-5 trains to extract deep features from flame images, while an adaptive selection fusion method on multi-layer features selects the most effective fused deep features. Subsequently, these fused deep features feed into DFC, constructing an offline recognition model for identifying combustion status. Finally, embedding this recognition system into an existing MSWI process data monitoring system enables online flame video recognition. Experimental results show remarkable accuracies: 93.80% and 95.08% for left and right grate furnace offline samples, respectively. When implemented in an online flame video recognition platform, it aptly meets recognition demands.

Suggested Citation

  • Xiaotong Pan & Jian Tang & Heng Xia & Tianzheng Wang, 2023. "Online Combustion Status Recognition of Municipal Solid Waste Incineration Process Using DFC Based on Convolutional Multi-Layer Feature Fusion," Sustainability, MDPI, vol. 15(23), pages 1-26, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16473-:d:1292044
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    References listed on IDEAS

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    1. Kapil Dev Sharma & Siddharth Jain, 2020. "Municipal solid waste generation, composition, and management: the global scenario," Social Responsibility Journal, Emerald Group Publishing Limited, vol. 16(6), pages 917-948, June.
    2. Parthasarathy Velusamy & Jagadeesan Srinivasan & Nithyaselvakumari Subramanian & Rakesh Kumar Mahendran & Muhammad Qaiser Saleem & Maqbool Ahmad & Muhammad Shafiq & Jin-Ghoo Choi, 2023. "Optimization-Driven Machine Learning Approach for the Prediction of Hydrochar Properties from Municipal Solid Waste," Sustainability, MDPI, vol. 15(7), pages 1-14, March.
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