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作者:

Han, Honggui (Han, Honggui.) | Zhang, Jiacheng (Zhang, Jiacheng.) | Hou, Ying (Hou, Ying.) | Qiao, Junfei (Qiao, Junfei.)

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摘要:

To achieve the excellent operational performance of the wastewater treatment process, optimal control has been considered a reliable method. However, there is a time-delay response of the operation performances to the process variables, leading to uncertainties of operational optimal objectives. It is difficult to obtain the optimal set-points due to the uncertain operational optimal objectives. Therefore, a kernel-density-estimation-based robust optimal control (KDE-ROC) method is proposed. First, a data-driven prediction strategy is developed to construct the uncertain operational optimal objectives. Based on the time-delay intervals, the uncertainties between process variables and operational optimal objectives are expressed. Second, a kernel-density-estimation-based robust optimization algorithm is designed to solve the uncertain operational optimal objectives. Then, the optimal set-points of process variables are obtained depending on the robustness index to reduce the influence of uncertainties. Third, an adaptive neural network controller is developed to track the optimal set-points of process variables. Finally, the proposed KDE-ROC is applied in benchmark simulation model No.1. In the experimental results, the optimal control performance of KDE-ROC is compared with some effective optimal control strategies to demonstrate its effectiveness.

关键词:

Optimization Optimal control Uncertainty time delay Delay effects wastewater treatment process (WWTP) Robust optimal control Effluents Process control robust optimization Indexes

作者机构:

  • [ 1 ] [Han, Honggui]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Engn Res Ctr Digital Community,Key Lab Computat In, Beijing 100022, Peoples R China
  • [ 2 ] [Zhang, Jiacheng]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Engn Res Ctr Digital Community,Key Lab Computat In, Beijing 100022, Peoples R China
  • [ 3 ] [Hou, Ying]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Engn Res Ctr Digital Community,Key Lab Computat In, Beijing 100022, Peoples R China
  • [ 4 ] [Qiao, Junfei]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Engn Res Ctr Digital Community,Key Lab Computat In, Beijing 100022, Peoples R China
  • [ 5 ] [Han, Honggui]Beijing Univ Technol, Beijing Lab Urban Mass Transit, Beijing 100022, Peoples R China
  • [ 6 ] [Zhang, Jiacheng]Beijing Univ Technol, Beijing Lab Urban Mass Transit, Beijing 100022, Peoples R China
  • [ 7 ] [Hou, Ying]Beijing Univ Technol, Beijing Lab Urban Mass Transit, Beijing 100022, Peoples R China
  • [ 8 ] [Qiao, Junfei]Beijing Univ Technol, Beijing Lab Urban Mass Transit, Beijing 100022, Peoples R China

通讯作者信息:

  • [Han, Honggui]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Engn Res Ctr Digital Community,Key Lab Computat In, Beijing 100022, Peoples R China;;[Han, Honggui]Beijing Univ Technol, Beijing Lab Urban Mass Transit, Beijing 100022, Peoples R China;;

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来源 :

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

ISSN: 1551-3203

年份: 2023

期: 4

卷: 19

页码: 5785-5796

1 2 . 3 0 0

JCR@2022

ESI学科: ENGINEERING;

ESI高被引阀值:19

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