Dioxin emission concentration soft measuring approach of municipal solid waste incineration based on selective ensemble kernel learning algorithm

TANG Jian; QIAO Junfei

doi:10.11949/j.issn.0438-1157.20181354

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Dioxin emission concentration soft measuring approach of municipal solid waste incineration based on selective ensemble kernel learning algorithm

Process system engineering | 更新时间：2026-07-24

- Dioxin emission concentration soft measuring approach of municipal solid waste incineration based on selective ensemble kernel learning algorithm
- CIESC Journal Vol. 70, Issue 2, Pages: 696-706(2019)
- 作者机构：
  
  1.北京工业大学信息学部，北京 100124
  2.计算智能与智能系统北京市重点实验室，北京 100124
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20181354
  CLC： TQ 028.8
- Received：16 November 2018，
  
  Revised：2018-11-26，
  
  Online First：16 June 2020，
  
  Published：05 February 2019
- 稿件说明：
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汤健, 乔俊飞. 基于选择性集成核学习算法的固废焚烧过程二英排放浓度软测量[J]. 化工学报, 2019, 70(2): 696-706

TANG Jian, QIAO Junfei. Dioxin emission concentration soft measuring approach of municipal solid waste incineration based on selective ensemble kernel learning algorithm[J]. CIESC Journal, 2019, 70(2): 696-706
汤健, 乔俊飞. 基于选择性集成核学习算法的固废焚烧过程二英排放浓度软测量[J]. 化工学报, 2019, 70(2): 696-706 DOI： 10.11949/j.issn.0438-1157.20181354.

TANG Jian, QIAO Junfei. Dioxin emission concentration soft measuring approach of municipal solid waste incineration based on selective ensemble kernel learning algorithm[J]. CIESC Journal, 2019, 70(2): 696-706 DOI： 10.11949/j.issn.0438-1157.20181354.

摘要

城市固废焚烧(MSWI)过程排放的二

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英(DXN)是被称为“世纪之毒”的持续性污染物。该过程的多阶段、多温度区间的物理化学特性导致DXN排放浓度的机理模型难以构建。工业实际中通常以月或季为周期耗时近1周时间在实验室以离线化验方式滞后检测。针对这些问题，提出了基于选择性集成(SEN)核学习算法的DXN排放浓度软测量方法。首先，基于先验知识给出候选核参数集和候选惩罚参数集，采用核学习算法构建基于这些超参数的候选子子模型；然后，耦合优化和加权算法对相同核参数的候选子子模型进行选择与合并，进而得到基于不同核参数的候选SEN子模型集合；最后，再次采用优化和加权算法获得结构与超参数自适应的多层SEN软测量模型。采用UCI平台水泥抗压强度和焚烧过程DXN数据验证了所提方法的有效性。

Abstract

Dioxin (DXN) emitted from the municipal solid waste incineration (MSWI) process is a persistent pollutant of the “century poison”. DXN is one of the highly toxic and persistent pollution. The principal model of DXN emission is difficult to obtained duo to the complex multi-stage and multi-temperature phase’s physical chemical characteristics. In practical

DXN emission concentration is off-line measured with month or quarter period by quantified national laboratory with long lag time delay. Aiming at these problems

a new DXN emission concentration soft measuring method based on selective ensemble (SEN) kernel learning algorithm is proposed. At first

candidate kernel parameters and regularization parameters are given based on prior knowledge. Then

candidate sub-sub-models based on these super parameters are constructed. Thirdly

coupled optimization and weighting algorithms are used to build SEN-sub-models. Finally

these SEN-sub-models are selective combined as final SEN model by using optimization and weighting algorithms again. Simulation results based on the concrete compression strength and incineration process DXN data validate effectiveness of the proposed approach.

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references

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