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

Xiaotong Pan, Jian Tang (), Heng Xia and Tianzheng Wang
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Xiaotong Pan: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Jian Tang: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Heng Xia: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Tianzheng Wang: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China

Sustainability, 2023, vol. 15, issue 23, 1-26

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.

Keywords: municipal solid waste incineration; combustion status; LeNet-5 network; deep forest classification; online flame video identification (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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