首页|基于仿真机理和改进回归决策树的二噁英排放建模

基于仿真机理和改进回归决策树的二噁英排放建模

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
城市固废焚烧(Municipal solid waste incineration,MSWI)过程是"世纪之毒"二噁英(Dioxin,DXN)的重要排放源之一.截止目前为止,DXN的演化机理和实时检测仍是尚未解决的难题.现有研究主要基于离线化验数据构建数据驱动模型,DXN的检测未有效结合燃烧过程机理.针对该问题,本文提出基于仿真机理和改进线性回归决策树(Linear re-gression decision tree,LRDT)的DXN排放建模.首先,采用基于床层固废燃烧模拟软件FLIC(Fluid dynamic inciner-ator code)和过程工程先进系统软件(Advanced system for process engineering Plus,Aspen Plus)耦合的数值仿真模型,获取蕴含多运行工况的虚拟机理数据;接着,利用虚拟机理数据构建基于改进LRDT的CO2、CO和O2燃烧状态表征变量模型;然后,以真实CO2、CO、O2作为输入和以DXN真值作为输出,构建多入单出LRDT的过程映射模型(Process mapping model,PMM),再利用该模型进行半监督学习和结构迁移得到机理映射模型1(Mechanism mapping modelsl,MMM1);最后,通过结构增量学习获得基于半监督迁移学习的MMM2模型.在实验室的半实物平台和北京某MSWI厂的边侧验证平台对所提方法进行了工业应用验证.实验结果证明了所提方法与研发的软测量系统可有效实现二噁英排放浓度在线检测.
Dioxin Emission Concentration Modeling Based on Simulation Mechanism and Improved Linear Regression Decision Tree
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.

Municipal solid waste incineration(MSWI)dioxin(DXN)combustion conditionnumerical simulation-based mechanismlinear regression decision tree(LRDT)semi-supervised transfer learning

夏恒、汤健、余文、乔俊飞

展开 >

北京工业大学信息学部 北京 100124 中国

北京工业大学智慧环保北京实验室 北京 100124 中国

墨西哥国立理工大学高级研究中心(CINVESTAV-IPN) 墨西哥 07360 墨西哥

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

国家自然科学基金国家自然科学基金国家自然科学基金

620730066217312062373017

2024

10.16383/j.aas.c230625

自动化学报
中国自动化学会 中国科学院自动化研究所

自动化学报

CSTPCD北大核心
影响因子：1.762
ISSN：0254-4156
年,卷(期)：2024.50(8)
  • 8