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soft measuring Method of Dioxin Emission Concentration for MSWI Process Based on RF and GBDT

Soft Measuring Method of Dioxin Emission Concentration for M...

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第32届中国控制与决策会议

作者： Heng Xia Jian Tang Junfei Qiao Aijun Yan Zihao Guo Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Dioxin（DXN） is a highly toxic pollutant emitted during municipal solid waste incinerator（MSWI） *** the actual industrial process,DXN emission concentration is measured through offline experiment analysis,which has shortcomings such as long time and high *** this study,a soft-sensing model of DXN emission concentration was established by using MSWI process *** forest（RF） and gradient boosting decision tree algorithms are used to construct ensemble learning-based DXN ***,RF tree sub-models are constructed base on random sampling and CART regression ***,Gradient boosting decision tree（GBDT） method is used to each RF sub-model,in which one gradient iteration is performed to reduce the prediction ***,a simple average combination strategy is performed on these RF and GBDT based ***,the soft measuring model of DXN emission concentration based on small samples and high-dimensional MSWI process data is *** proposed method can both reduce model variance and eliminate prediction *** experimental results show that the proposed method can further improve the prediction performance and generalization ability.

关键词： Municipal solid waste incinerator(MSWI) soft measuring model Dioxin(DXN) emission concentration Random forest(RF) Gradient boosting decision tree(GBDT)

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Dioxin Emission Concentration soft measuring Method Based on Selective Ensemble Least Square Support Vector Machine Algorithm

Dioxin Emission Concentration Soft Measuring Method Based on...

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第37届中国控制会议

作者： Jian Tang Junfei Qiao Zihao Guo Aijun Yan Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Dioxin（DXN） is a highly toxic and persistent pollutant discharged from municipal solid waste incineration（MSWI）.The first principal model of DXN is difficult to establish due to the complex physical and chemical characteristics of the incineration process. In the practical process, DXN emission concentration is off-line detected with monthly or seasonal *** at such small sample modeling problem, a soft measuring method based on selective ensemble（SEN） least square support vector machine（LSSVM） for modeling DXN emission concentration is proposed. At first, candidate training sub-samples are produced from original training samples. Then, different candidate sub-sub-models based on the same kernel parameter and regularization parameter are constructed by using LSSVM. Thirdly, ensemble sub-models are selected by using the genetic algorithm optimization tool box and prior knowledge. Finally, these ensemble sub-models are combined by using partial least squares algorithm in terms of reduction con-linearity among different prediction outputs. Simulation results show effectiveness of the proposed approach by using dataset in reference [18].

关键词： Municipal solid waste incineration (MSWI) dioxin (DXN) selective ensemble least square support vector machine soft measuring model

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