化工学报2019，Vol.70Issue(2)：696-706,11.DOI:10.11949/j.issn.0438⁃1157.20181354
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
Dioxin emission concentration soft measuring approach of municipal solid waste incineration based on selective ensemble kernel learning algorithm
摘要
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.关键词
城市固废焚烧/过程系统/二英/参数估值/选择性集成/废物处理Key words
municipal solid waste incineration/ process systems/ dioxin/ parameter estimation/ selective ensemble/waste treatment分类
化学化工引用本文复制引用
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):696-706,11.基金项目
科学技术部国家重点研发计划项目(2018YFC1900801) （2018YFC1900801）
国家自然科学基金项目(61573364,61873009) （61573364,61873009）
矿冶过程自动控制技术国家(北京)重点实验室项目(BGRIMM-KZSKL-2017-07) （北京）
