Soft Sensing Method of Dioxin Emission in Municipal Solid Waste Incineration Process Based on Broad Hybrid Forest Regression

被引：0
|
作者：
Xia H. [1 ,2 ,3 ]
Tang J. [1 ,2 ,3 ]
Cui C.-L. [1 ,2 ,3 ]
Qiao J.-F. [1 ,2 ,3 ]
机构：
[1] Faculty of Information Technology, Beijing University of Technology, Beijing
[2] Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing
[3] Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing University of Technology, Beijing
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2023年 / 49卷 / 02期
基金：
中国国家自然科学基金;
关键词：
broad hybrid forest regression; broad learning; dioxin emission modeling; incremental learning; latent feature; Municipal solid waste incineration;
D O I：
10.16383/j.aas.c220012
中图分类号：
学科分类号：
摘要：
Dioxin is a trace organic pollutant emitted from municipal solid waste incineration process. Limited by the complexity and high cost of relative technology, the big time delay of dioxin emission concentration detection has become one of the key factors restricting the optimize control of municipal solid waste incineration process. Although the data-driven soft sensing model with the characteristics of low cost, fast response and high precision can effectively solve the above problems, the dioxin modeling method must fit the small sample and high-dimensional characteristics of the modeling data. In this paper, a broad hybrid forest regression soft sensing method is proposed, which consists of feature mapping layer, latent feature extraction layer, feature enhancement layer and incremental learning layer. Firstly, a hybrid forest group composed of random forest and completely random forest is constructed for high-dimensional feature mapping. Secondly, the latent features extraction of the fully connected mixed matrix is carried out according to the contribution rate, and the information measurement criterion is used to ensure the maximum transmission and minimize redundancy of potential valuable information. Thus, the model complexity and computational consumption are reduced. Then, the feature enhancement layer is trained based on the extracted potential information to enhance the feature representation ability. Finally, the incremental learning layer is constructed by using incremental learning strategy, and the weight matrix is obtained by using the Moore-Penrose pseudo inverse. The experimental results on high-dimensional benchmark and dioxin datasets of municipal solid waste incineration process show the effectiveness and superiority of the proposed method. © 2023 Science Press. All rights reserved.
引用
收藏
页码：343 / 365
页数：22
相关论文
共 55 条
  • [1] Kammen D M, Sunter D A., City-integrated renewable energy for urban sustainability, Science, 352, 6288, pp. 922-928, (2016)
  • [2] Tobias W, Ludwig K L, Robert B, Esther E, Luca F, Bodo H, Et al., Persistence of engineered nanoparticles in a municipal solid-waste incineration plant, Nature Nanotechnology, 7, 8, (2012)
  • [3] Qiao Jun-Fei, Guo Zi-Hao, Tang Jian, Dioxin emission concentration measurement approaches for municipal solid wastes incineration process: A survey, Acta Automatica Sinica, 46, 6, pp. 1063-1089, (2020)
  • [4] Wang B, Wang P L, Xie L H, Lin R B, Lv J, Li J R, Et al., A stable zirconium based metal-organic framework for specific recognition of representative polychlorinated dibenzo-p-dioxin molecules, Nature Communications, 10, 1, (2019)
  • [5] Wei J X, Li H, Liu J G., Fate of dioxins in a municipal solid waste incinerator with state-of-the-art air pollution control devices in China, Environmental Pollution, 289, pp. 1-10, (2021)
  • [6] Dai Wei, Lu Wen-Jie, Fu Jun, Ma Xiao-Ping, Multi-rate layered optimal operational control of industrial processes, Acta Automatica Sinica, 45, 10, pp. 1946-1959, (2019)
  • [7] Xue W Q, Fan J L, Victor G L, Li J N, Jiang Y, Chai T Y, Et al., New methods for optimal operational control of industrial processes using reinforcement learning on two time scales, IEEE Transactions on Industrial Informatics, 16, 5, pp. 3085-3099, (2020)
  • [8] Ambient air and flue gas. Determination of polychlorinated dibenzo-p-dioxions (PCDDs) and ploychlorinated dibenzofurans (PCDFs) Isotope dilution HRCCHRMS, (2008)
  • [9] Li A-Dan, Hong Wei, Wang Jing, Online detection of dioxins and their related substances by laser desorption/laser ionization mass spectrometry, Journal of Yanshan University, 39, 6, pp. 511-515, (2015)
  • [10] Nakui H, Koyama H, Takakura A, Watanabe N., Online measurements of low-volatile organic chlorine for dioxin monitoring at municipal waste incinerators, Chemosphere, 85, 2, pp. 151-155, (2011)
123456