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作者:

Xia, Heng (Xia, Heng.) | Tang, Jian (Tang, Jian.) | Yu, Wen (Yu, Wen.) | Qiao, Junfei (Qiao, Junfei.) (学者:乔俊飞)

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EI Scopus SCIE

摘要:

The real-time detection technique and comprehensive characterization of dioxin (DXN) emission concentration during the municipal solid waste incineration process persist as unresolved challenges. Prevailing research predominantly relies on data-driven models, often overlooking the potential benefits derived from fusing combustion mechanism knowledge. To confront this issue, we propose a hybrid modeling strategy that fuses a simulator-based mechanism model with an enhanced regression decision tree-based data model. This approach aims to predict DXN emission concentrations while accommodating diverse time-scaled measurement requirements. Based on virtual mechanism data obtained via numerical simulation models coupling FLIC and Aspen Plus, we constructed a white-box surrogate model utilizing a multiple-input multiple-output linear regression decision tree (LRDT). To establish a relationship with DXN emission concentration, we employed a semisupervised transfer learning mapping model. It was then fused with a novel ensemble LRDT model based on real historical data by using a constrained incremental random weight neural network. The efficacy of this modeling strategy was validated through an industrial application case study conducted in Beijing.

关键词:

Numerical models municipal solid waste incineration (MSWI) MIMO communication Data models Mathematical models multidemand modeling Predictive models Solid modeling Combustion linear regression decision tree (LRDT) mechanism-driven (MD) and data-driven (DD) numerical simulation Dioxin (DXN) semisupervised transfer learning

作者机构:

  • [ 1 ] [Xia, Heng]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 ] [Qiao, Junfei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Yu, Wen]CINVESTAV IPN Natl Polytech Inst, Dept Control Automat, Mexico City 07360, Mexico

通讯作者信息:

  • [Tang, Jian]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China;;[Yu, Wen]CINVESTAV IPN Natl Polytech Inst, Dept Control Automat, Mexico City 07360, Mexico

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来源 :

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

ISSN: 0278-0046

年份: 2024

7 . 7 0 0

JCR@2022

被引次数:

WoS核心集被引频次: 4

SCOPUS被引频次: 4

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:

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