• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Zhang, Runyu (Zhang, Runyu.) | Tang, Jian (Tang, Jian.) (学者:汤健) | Xia, Heng (Xia, Heng.) | Chen, Jiakun (Chen, Jiakun.) | Yu, Wen (Yu, Wen.) | Qiao, Junfei (Qiao, Junfei.)

收录:

EI Scopus SCIE

摘要:

Carbon monoxide (CO) is a hazardous gas discharged during municipal solid waste incineration (MSWI). Its emission concentration serves as a vital indicator for assessing the stability of the MSWI process. Therefore, accurate prediction of CO emissions is crucial. While existing research predominantly relies on historical real data -driven models, it often overlooks the effective utilization of the combustion mechanism. This article introduced a novel approach: a heterogeneous ensemble prediction model that integrates virtual and real data. Firstly, virtual mechanism data was obtained through a multi -condition mechanism model constructed using coupled numerical simulation software of FLIC and Aspen Plus. Secondly, based on this virtual mechanism data, a linear regression decision tree (LRDT) algorithm was employed to establish the mechanism mapping model. Simultaneously, a real historical data -driven model based on a long short-term memory (LSTM) neural network algorithm was developed. In the offline training verification phase, the heterogeneous models were combined using an inequality -constrained random weighted neural network (CIRWNN) after aligning virtual and real samples representing operating conditions based on the k -nearest neighbor (KNN) approach. Subsequently, in the online testing verification stage, CO online prediction was achieved by ensemble the LRDT-based mechanism mapping model and. the LSTM-based historical data -driven model. The proposed method's effectiveness and rationality were validated through an industrial case study of MSWI process in Beijing.

关键词:

Municipal solid waste incineration (MSWI) Heterogeneous ensemble model Hybrid -drive Virtual mechanism data Carbon monoxide (CO) Real historical data Coupled numerical simulation

作者机构:

  • [ 1 ] [Zhang, Runyu]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Tang, Jian]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Xia, Heng]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Chen, Jiakun]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 5 ] [Qiao, Junfei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 6 ] [Zhang, Runyu]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 7 ] [Tang, Jian]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 8 ] [Xia, Heng]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 9 ] [Chen, Jiakun]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 10 ] [Qiao, Junfei]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 11 ] [Yu, Wen]IPN, Natl Polytech Inst, Dept Control Automat, CINVESTAV, Mexico City 07360, Mexico

通讯作者信息:

  • 汤健

    [Tang, Jian]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

电子邮件地址:

查看成果更多字段

相关关键词:

来源 :

JOURNAL OF CLEANER PRODUCTION

ISSN: 0959-6526

年份: 2024

卷: 445

1 1 . 1 0 0

JCR@2022

被引次数:

WoS核心集被引频次: 9

SCOPUS被引频次: 11

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 2

归属院系:

在线人数/总访问数:280/4911184
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司