自动化学报2024，Vol.50Issue(8)：1601-1619,19.DOI:10.16383/j.aas.c230625
基于仿真机理和改进回归决策树的二噁英排放建模
Dioxin Emission Concentration Modeling Based on Simulation Mechanism and Improved Linear Regression Decision Tree
摘要
Abstract
Municipal solid waste incineration(MSWI)process has been one of the important emission sources of di-oxin(DXN)in terms of century posion.Untill now,the evolution mechanism and real-time detection of DXN emis-sion concentration are still unsolved challenges.Existing studies mainly rely on available data to build data-driven modeling,and how to effectively combine the mechanism of combustion process for DXN detection is a problem that is not considered.To solve this problem,this article proposes DXN emission modelling method based on simu-lation mechanism and improved linear regression decision tree(LRDT).First,a numerical simulation model based on coupling fluid dynamic incinerator code(FLIC)and advanced system for process engineering Plus(Aspen Plus)software is used to obtain virtual mechanism data with multiple operating conditions.Then,virtual mechanism data is used to construct an improved LRDT combustion state representation variable CO2,CO,and O2 model.Next,a process mapping model(PMM)based on multiple input single output LRDT is constructed using real CO2,CO,and O2 as input and DXN as output.Semi-supervised learning and structural transfer learning based on PMM are used to obtain the mechanism mapping modelsl(MMM1).Finally,the final MMM2 based on semi-supervised transfer learning is obtained by the structural growth learning of the MMM1.The proposed method was validated for industrial application on a hardware-in-loop simulation platform in the laboratory and an edge verification plat-form at an MSWI plant in Beijing.The experimental results show that the proposed method and the developed soft measurement system can effectively realize the on-line detection of DXN emission concentration.关键词
城市固废焚烧/二噁英/燃烧状态/数值仿真机理/线性回归决策树/半监督迁移学习Key words
Municipal solid waste incineration(MSWI)/dioxin(DXN)/combustion condition/numerical simulation-based mechanism/linear regression decision tree(LRDT)/semi-supervised transfer learning引用本文复制引用
夏恒,汤健,余文,乔俊飞..基于仿真机理和改进回归决策树的二噁英排放建模[J].自动化学报,2024,50(8):1601-1619,19.基金项目
国家自然科学基金(62073006,62173120,62373017)资助Supported by National Natural Science Foundation of China(62073006,62173120,62373017) （62073006,62173120,62373017）
