Municipal solid waste incineration state recognition method based on multilayer preprocessing

doi:10.11772/j.issn.1001-9081.2025030368

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (3): 940-949.DOI: 10.11772/j.issn.1001-9081.2025030368

• Multimedia computing and computer simulation • Previous Articles Next Articles

Municipal solid waste incineration state recognition method based on multilayer preprocessing

Jian ZHANG¹(), Jianbo YU¹, Jian TANG²

^1.School of Computer Science/ School of Cyber Science and Engineering，Nanjing University of Information Science and Technology，Nanjing Jiangsu 210044，China
^2.School of Information Science and Technology，Beijing University of Technology，Beijing 100124，China

Received:2025-04-07 Revised:2025-06-16 Accepted:2025-06-25 Online:2025-07-15 Published:2026-03-10
Contact: Jian ZHANG
About author:YU Jianbo， born in 2001， M. S. candidate. His research interests include machine learning， image classification.
TANG Jian， born in 1974， Ph. D.， professor. His research interests include small sample data modeling， intelligent control of municipal solid waste treatment process.
Supported by:
National Natural Science Foundation of China(62073006)

基于多层预处理的城市固废焚烧状态识别方法

张健¹(), 于剑波¹, 汤健²

^1.南京信息工程大学计算机学院、网络空间安全学院，南京 210044
^2.北京工业大学信息科学技术学院，北京 100124

通讯作者: 张健
作者简介:于剑波（2001—），男，江苏盐城人，硕士研究生，CCF会员，主要研究方向：机器学习、图像分类
汤健（1974—），男，辽宁北票人，教授，博士生导师，博士，主要研究方向：小样本数据建模、固废处理过程智能控制。
基金资助:
国家自然科学基金资助项目(62073006)

Abstract

Abstract:

Due to the strong contamination， high noise level， excessive exposure， and other problems in flame images from domestic Municipal Solid Waste Incineration （MSWI） processes， traditional target recognition methods are difficult to apply to them. Therefore， an MSWI incineration image classification framework — SAswin with Multilayer Preprocessing Network （SAswin-MPNet） was proposed. Firstly， a Transformer-based Hybrid Attention Super-Resolution Transformer （HASRT） module was designed to perform super-resolution reconstruction to the images. Secondly， a Practical Exposure Correction （PEC） module was introduced to correct the exposure of high-resolution MSWI images， thereby obtaining multilayer preprocessed data. Additionally， a validation algorithm was designed to compare and test the preprocessed images and the originals， and the images meeting a validation threshold were used to replace the originals， thereby obtaining a multilayer preprocessed dataset. Finally， an SAswin classification network was constructed to recognize incineration states. Experimental results based on actual operational data from an MSWI power plant comparing with ResNet-34， ResNet-50， ConvNeXt， ViT （Vision Transformer）， Swin-T （Swin-Tiny）， and EVA-02 （Enhanced Visual Assistant-02） show that SAswin-MPNet achieves the optimal MSWI image incineration state recognition accuracy and F1-score.

Key words: Municipal Solid Waste Incineration (MSWI), image preprocessing, image classification, Transformer, deep learning

摘要：

由于国内城市固废焚烧（MSWI）过程的火焰图像具有强污染、高噪声和曝光度过高等问题，传统目标识别方法难以适用。因此，提出一种MSWI焚烧图像分类框架——基于多层预处理的SAswin网络（SAswin-MPNet）。首先，设计基于Transformer的混合注意力超分辨率重建（HASRT）模块对图像进行超分辨率的重建处理；其次，引入实用曝光校正（PEC）模块对高分辨率MSWI图像进行曝光度校正，从而获得多层预处理数据；此外，设计检验算法将预处理后的图像与原图像进行对照检验，将达到检验阈值的图像替换原图像，从而获得多层预处理数据集；最后，构建SAswin分类网络以识别燃烧状态。基于某MSWI电厂实际运行数据与ResNet-34、ResNet-50、ConvNeXt、ViT（Vision Transformer）、Swin-T（Swin-Tiny）和EVA-02（Enhanced Visual Assistant-02）进行对比的实验结果表明，SAswin-MPNet的MSWI图像燃烧状态识别的准确率与F1分数取得了最优结果。

关键词: 城市固废焚烧, 图像预处理, 图像分类, Transformer, 深度学习

CLC Number:

TP399

Jian ZHANG, Jianbo YU, Jian TANG. Municipal solid waste incineration state recognition method based on multilayer preprocessing[J]. Journal of Computer Applications, 2026, 46(3): 940-949.

张健, 于剑波, 汤健. 基于多层预处理的城市固废焚烧状态识别方法[J]. 《计算机应用》唯一官方网站, 2026, 46(3): 940-949.

Figures/Tables 16

References 35

[1]	乔俊飞，郭子豪，汤健. 面向城市固废焚烧过程的二噁英排放浓度检测方法综述［J］. 自动化学报， 2020， 46（6）： 1063-1089.
	QIAO J F， GUO Z H， TANG J. Dioxin emission concentration measurement approaches for municipal solid wastes incineration process： a survey ［J］. Acta Automatica Sinica， 2020， 46（6）： 1063-1089.
[2]	MENG X， TANG J， QIAO J. NOx emissions prediction with a brain-inspired modular neural network in municipal solid waste incineration processes ［J］. IEEE Transactions on Industrial Informatics， 2022， 18（7）： 4622-4631.
[3]	QIAO J， CUI Y， MENG X. Dynamic multi-objective operation optimization of municipal solid waste incineration process based on transfer learning ［J］. IEEE Transactions on Automation Science and Engineering， 2025， 22： 9338-9352.
[4]	LU J W， ZHANG S， HAI J， et al. Status and perspectives of municipal solid waste incineration in China： a comparison with developed regions ［J］. Waste Management， 2017， 69： 170-186.
[5]	NZIHOU A， THEMELIS N J， KEMIHA M， et al. Dioxin emissions from Municipal Solid Waste Incinerators （MSWIs） in France ［J］. Waste Management， 2012， 32（12）： 2273-2277.
[6]	KOLEKAR K A， HAZRA T， CHAKRABARTY S N. A review on prediction of municipal solid waste generation models ［J］. Procedia Environmental Sciences， 2016， 35： 238-244.
[7]	YIN C， ROSENDAHL L， KÆR K， et al. Mathematical modeling and experimental study of biomass combustion in a thermal 108 MW grate-fired boiler［J］. Energy and Fuels， 2008， 22（2）： 1380-1390.
[8]	LI K， DENG J， ZHU Y， et al. Utilization of municipal solid waste incineration fly ash with different pretreatments with gold tailings and coal fly ash for environmentally friendly geopolymers ［J］. Waste Management， 2025， 194： 342-352.
[9]	TANG J， WANG T， XIA H， et al. An overview of artificial intelligence application for optimal control of municipal solid waste incineration process ［J］. Sustainability， 2024， 16（5）： No.2042.
[10]	KHAN A， SOHAIL A， ZAHOORA U， et al. A survey of the recent architectures of deep convolutional neural networks ［J］. Artificial Intelligence Review， 2020， 53（8）： 5455-5516.
[11]	YANG W， TANG J， XIA H， et al. Combustion status recognition of MSWI process based on flame image by using YOLOv5 ［C］// Proceedings of the 36th Chinese Control and Decision Conference. Piscataway： IEEE， 2024： 2363-2368.
[12]	ZHANG J， YU J， YU G， et al. An image classification algorithm for MSWI based on ResNet and attention mechanism ［C］// Proceedings of the 36th Chinese Control and Decision Conference. Piscataway： IEEE， 2024： 1725-1730.
[13]	YAN A， HU K， WANG D. Monitoring model based on data-driven optimization stochastic configuration network and its applications ［J］. IEEE Sensors Journal， 2025， 25（6）： 10087-10096.
[14]	郭海涛，汤健，丁海旭，等. 基于混合数据增强的MSWI过程燃烧状态识别［J］. 自动化学报， 2024， 50（3）： 560-575.
	GUO H T， TANG J， DING H X， et al. Combustion states recognition method of MSWI process based on mixed data enhancement ［J］. Acta Automatica Sinica， 2024， 50（3）： 560-575.
[15]	汤健，郭海涛，夏恒，等. 面向工业过程的图像生成及其应用研究综述［J］. 自动化学报， 2024， 50（2）： 211-240.
	TANG J， GUO H T， XIA H， et al. Image generation and its application research for industrial process： a survey ［J］. Acta Automatica Sinica， 2024， 50（2）： 211-240.
[16]	ZHOU H， TANG Y. An enhanced spatial depth convolution and super-resolution image reconstruction algorithm for Beidou robot fire monitoring ［C］// Proceedings of the 5th International Conference on Computer Engineering and Application. Piscataway： IEEE， 2024： 975-980.
[17]	CHEN X， WANG X， ZHOU J， et al. Activating more pixels in image super-resolution Transformer ［C］// Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2023： 22367-22377.
[18]	LIU Z， LIN Y， CAO Y， et al. Swin Transformer： hierarchical Vision Transformer using shifted windows ［C］// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway： IEEE， 2021： 9992-10002.
[19]	VASWANI A， SHAZEER N， PARMAR N， et al. Attention is all you need ［C］// Proceedings of the 31st Conference on Neural Information Processing Systems. Red Hook： Curran Associates Inc.， 2017： 6000-6010.
[20]	ZHAO Y， WANG G， TANG C， et al. A battle of network structures： An empirical study of CNN， Transformer， and MLP［EB/OL］. ［2025-02-14］..
[21]	XIAO T， SINGH M， MINTUN E， et al. Early convolutions help Transformers see better ［C］// Proceedings of the 35th International Conference on Neural Information Processing Systems. Red Hook： Curran Associates Inc.， 2021： 30392-30400.
[22]	YUAN K， GUO S， LIU Z， et al. Incorporating convolution designs into Visual Transformers ［C］// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway： IEEE， 2021： 559-568.
[23]	ZU B， CAO T， LI Y， et al. SwinT-SRNet： a classification network based on Swin Transformer backbone ［J］. Engineering Applications of Artificial Intelligence， 2024， 133： No.108041.
[24]	SUN D， LU G， ZHOU H， et al. Quantitative assessment of flame stability through image processing and spectral analysis ［J］. IEEE Transactions on Instrumentation and Measurement， 2015， 64（12）： 3323-3333.
[25]	AFIFI M， DERPANIS K， OMMER B， et al. Learning multi-scale Photo exposure correction ［C］// Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2021： 9153-9163.
[26]	MA L， MA T， XUE X， et al. Practical exposure correction： great truths are always simple ［EB/OL］. ［2025-02-15］..
[27]	SHI W， CABALLERO J， HUSZÁR F， et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network ［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2016： 1874-1883.
[28]	HENDRYCKS D， GIMPEL K. Gaussian Error Linear Units （GELUs）［EB/OL］. ［2025-01-14］..
[29]	WOO S， PARK J， LEE J Y， et al. CBAM： convolutional block attention module ［C］// Proceedings of the 2018 European Conference on Computer Vision， LNCS 11211. Cham： Springer， 2018： 3-19.
[30]	PAN X， TANG J， XIA H， et al. Construction of flame image classification criteria and reference database for municipal solid waste incineration process ［C］// Proceedings of the 35th Chinese Control and Decision Conference. Piscataway： IEEE， 2023： 343-348.
[31]	HE K， ZHANG X， REN S， et al. Deep residual learning for image recognition ［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2016： 770-778.
[32]	LIU Z， MAO H， WU C Y， et al. A ConvNet for the 2020s ［C］// Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2022： 11966-11976.
[33]	DOSOVITSKIY A， BEYER L， KOLESIKOV A， et al. An image is worth 16x16 words： Transformers for image recognition at scale［EB/OL］. ［2025-02-25］..
[34]	FANG Y， SUN Q， WANG X， et al. EVA-02： a visual representation for neon genesis ［J］. Image and Vision Computing， 2024， 149： No.105171.
[35]	PAN X， TANG J， XIA H， et al. Online combustion status recognition of municipal solid waste incineration process using DFC based on convolutional multi-layer feature fusion ［J］. Sustainability， 2023， 15（23）： No.16473.

来源	样本数				总样本数
来源	正常	窜烧	偏烧	闷烧	总样本数
左炉排	655	1 044	1 176	414	3 289
右炉排	564	534	1 002	585	2 685

来源	样本数				总样本数
来源	正常	窜烧	偏烧	闷烧	总样本数
左炉排	655	1 044	1 176	414	3 289
右炉排	564	534	1 002	585	2 685

数据集	方法	无预训练权重		有预训练权重
数据集	方法	F1分数	准确率	F1分数	准确率
左炉数据	ResNet-34	94.32	94.89	94.66	95.10
	ResNet-50	93.71	94.38	94.02	94.68
	ConvNeXt	94.34	94.98	94.68	95.20
	ViT	93.86	94.47	94.78	95.32
	Swin-T	93.88	94.50	95.20	95.65
	EVA-02	94.79	95.23	95.53	95.87
	SAswin-MPNet	95.33	95.80	95.65	96.14
右炉数据	ResNet-34	94.33	94.53	95.15	95.31
	ResNet-50	94.45	94.64	94.68	94.82
	ConvNeXt	95.03	95.23	95.38	95.49
	ViT	94.06	94.26	95.32	95.53
	Swin-T	94.69	94.86	95.65	95.83
	EVA-02	95.39	95.53	95.79	95.94
	SAswin-MPNet	95.97	96.09	96.10	96.28

数据集	方法	无预训练权重		有预训练权重
数据集	方法	F1分数	准确率	F1分数	准确率
左炉数据	ResNet-34	94.32	94.89	94.66	95.10
	ResNet-50	93.71	94.38	94.02	94.68
	ConvNeXt	94.34	94.98	94.68	95.20
	ViT	93.86	94.47	94.78	95.32
	Swin-T	93.88	94.50	95.20	95.65
	EVA-02	94.79	95.23	95.53	95.87
	SAswin-MPNet	95.33	95.80	95.65	96.14
右炉数据	ResNet-34	94.33	94.53	95.15	95.31
	ResNet-50	94.45	94.64	94.68	94.82
	ConvNeXt	95.03	95.23	95.38	95.49
	ViT	94.06	94.26	95.32	95.53
	Swin-T	94.69	94.86	95.65	95.83
	EVA-02	95.39	95.53	95.79	95.94
	SAswin-MPNet	95.97	96.09	96.10	96.28

模型	类别	精确率	召回率	特异度	F1分数	准确率
ConvNeXt	正常	93.08	94.91	98.19	93.99	—
	偏烧	97.11	94.26	98.39	95.67	—
	窜烧	95.14	94.23	98.27	94.68	—
	闷烧	90.32	95.30	97.95	92.74	—
	整体	93.91	94.68	98.20	94.27	94.56
EVA-02	正常	94.33	95.57	98.53	94.95	—
	偏烧	97.28	95.13	98.47	96.19	—
	窜烧	96.04	95.37	98.59	95.71	—
	闷烧	91.94	95.90	98.31	93.88	—
	整体	94.90	95.49	98.48	95.18	95.41
SAswin-MPNet	正常	95.03	95.65	98.72	95.34	—
	偏烧	97.44	96.28	98.55	96.86	—
	窜烧	96.47	95.37	98.75	95.92	—
	闷烧	93.24	96.60	98.59	94.89	—
	整体	95.55	95.98	98.65	95.75	95.97

Municipal solid waste incineration state recognition method based on multilayer preprocessing

基于多层预处理的城市固废焚烧状态识别方法

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 16

References 35

Related Articles 15

Recommended Articles

Metrics

方法	HASRT	PEC	预处理筛选模块	预训练	精确率/%	召回率/%	特异度/%	F1分数/%	用时/s	准确率/%
SAswin	×	×	×	×	94.32	94.99	98.31	94.64	0.183	94.89
SAswin+HASRT	√	×	×	×	94.60	95.21	98.42	94.89	18.831	95.21
SAswin+PEC	×	√	×	×	94.66	95.13	98.37	94.85	0.325	95.12
SAswin+HASRT+PEC	√	√	×	×	95.15	95.55	98.55	95.34	19.323	95.63
SAswin-MPNet	√	√	√	×	95.54	95.97	98.66	95.75	19.477	95.97
SAswin-MPNet	√	√	√	√	95.65	96.11	98.70	95.87	19.465	96.08

[1]	Songsen YU, Huang HE, Guopeng XUE, Hengtuo CUI. Quantitation and grading method for ceramic tile chromatic aberration based on improved fractal encoding network [J]. Journal of Computer Applications, 2026, 46(3): 959-968.
[2]	Jixin GUO, Ting ZHANG. Transformer image dehazing based on component collaborative optimization pruning [J]. Journal of Computer Applications, 2026, 46(3): 933-939.
[3]	Ping HUANG, Qing LI, Haifeng QIU, Chengsi WANG, Anzi HUANG, Long FAN. Lightweight method for transmission line defect detection [J]. Journal of Computer Applications, 2026, 46(3): 969-979.
[4]	Hanqing LIU, Guoming SANG, Yijia ZHANG. Remote sensing image captioning model combining dense multi-scale feature fusion and feature knowledge-enhanced Transformer [J]. Journal of Computer Applications, 2026, 46(3): 741-749.
[5]	Min CHEN, Xiaolin QIN, Shaohan LI, Hao YANG, Taohong LI. Review of deep learning applications in severe convective weather prediction [J]. Journal of Computer Applications, 2026, 46(3): 980-992.
[6]	Yongwei JIANG, Xiaoqing CHEN, Linjie FU. Elastic medical image registration model with high-frequency preservation based on spectrum decomposition [J]. Journal of Computer Applications, 2026, 46(3): 924-932.
[7]	Haoqian JIANG, Dong ZHANG, Guanyu LI, Heng CHEN. SetaCRS： Conversational recommender system with structure-enhanced hierarchical task-oriented prompting strategy [J]. Journal of Computer Applications, 2026, 46(2): 368-377.
[8]	Jincheng FU, Shiyou YANG. Short-term wind power prediction using hybrid model based on Bayesian optimization and feature fusion [J]. Journal of Computer Applications, 2026, 46(2): 652-658.
[9]	Ming LI, Mengqi WANG, Aili ZHANG, Hua REN, Yuqiang DOU. Image steganography method based on conditional generative adversarial networks and hybrid attention mechanism [J]. Journal of Computer Applications, 2026, 46(2): 475-484.
[10]	Jie CAO, Lingfeng XIE, Bingjin WANG, Changhe ZHANG, Zidong YU, Chao DENG. Adolescent scoliosis screening method considering class imbalance and background diversity [J]. Journal of Computer Applications, 2026, 46(2): 630-639.
[11]	Zeyi GUO, Fenglian LI, Lichun XU. Double decision mechanism-based deep symbolic regression algorithm [J]. Journal of Computer Applications, 2026, 46(2): 406-415.
[12]	Feng HAN, Yongfeng BU, Haoxiang LIANG, Shuwen HUANG, Zhaoyang ZHANG, Shijie SUN. Vehicle trajectory anomaly detection based on multi-level spatio-temporal interaction dependency [J]. Journal of Computer Applications, 2026, 46(2): 604-612.
[13]	Xiaolei CHEN, Zhiwei ZHENG, Xue HUANG, Zhenbin QU. Panoramic video super-resolution network combining spherical alignment and adaptive geometric correction [J]. Journal of Computer Applications, 2026, 46(2): 528-535.
[14]	Jun WU, Chuan ZHAO. Small object detection method based on improved DETR algorithm [J]. Journal of Computer Applications, 2026, 46(2): 564-571.
[15]	Quanjie LIU, Zhaoyi GU, Chunyuan WANG. Unsafe driving behavior detection under complex lighting conditions [J]. Journal of Computer Applications, 2026, 46(2): 613-619.