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作者:

Sun, Jian (Sun, Jian.) | Meng, Xi (Meng, Xi.) | Qiao, Junfei (Qiao, Junfei.)

收录:

EI Scopus SCIE

摘要:

Municipal solid waste incineration (MSWI) is a dynamic industrial process involving complex physical and chemical reactions. Due to the uncertain municipal solid waste composition and dynamic operation conditions, it is difficult to guarantee optimal operation for the MSWI process. To solve this problem, a data-driven optimal control scheme is proposed with a hierarchical control structure. In the set points optimization stage, an online adaptive fuzzy neural network is designed to construct the objective functions, including combustion efficiency and NOx emission concentration. An adaptive mutation particle swarm optimization algorithm is developed to obtain the optimal set points of oxygen content in flue gas. In the control stage, a double long short-term memory neural networks-based model predictive control (DLSTM-MPC) strategy is exploited. A self-organizing long short-term memory network is employed to predict the oxygen content in flue gas with a compact structure. To reduce the influences from dynamic disturbances and further improve the tracking control performance, another long short-term memory neural network is established to correct the prediction results. Finally, the set points optimization method and DLSTM-MPC strategy are combined to realize the optimal operation of the MSWI process. The effectiveness of the proposed optimal control scheme is verified by real industrial data. The experimental results demonstrate that the optimal control scheme can achieve promising tracking control performance of oxygen content in flue gas and improve the operational performance of the MSWI process.

关键词:

Adaptive fuzzy neural network (AFNN) data-driven model predictive control (MPC) model correction adaptive mutation particle swarm optimization (AMPSO) algorithm municipal solid waste incineration (MSWI)

作者机构:

  • [ 1 ] [Sun, Jian]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Meng, Xi]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 ] [Sun, Jian]Minist Educ, Engn Res Ctr Intelligence Percept & Autonomous Con, Beijing 100124, Peoples R China
  • [ 5 ] [Meng, Xi]Minist Educ, Engn Res Ctr Intelligence Percept & Autonomous Con, Beijing 100124, Peoples R China
  • [ 6 ] [Qiao, Junfei]Minist Educ, Engn Res Ctr Intelligence Percept & Autonomous Con, Beijing 100124, Peoples R China

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来源 :

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

ISSN: 1551-3203

年份: 2023

期: 12

卷: 19

页码: 11444-11454

1 2 . 3 0 0

JCR@2022

被引次数:

WoS核心集被引频次: 19

SCOPUS被引频次: 23

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

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