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作者:

Tian, Hao (Tian, Hao.) | Tang, Jian (Tang, Jian.) | Xia, Heng (Xia, Heng.) | Wang, Tianzheng (Wang, Tianzheng.) | Yu, Wen (Yu, Wen.) | Wu, Zhiwei (Wu, Zhiwei.)

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摘要:

This paper tackles the challenge of effectively regulating furnace temperature in the municipal solid waste incineration (MSWI) process. It proposes an approach that integrates reinforcement learning with traditional PID control. Firstly, an analysis of the characteristics of furnace temperature control is conducted to identify the key manipulated variables affecting it. Subsequently, a control strategy specifically tailored for furnace temperature is developed, employing online adaptive PID with a BPNN-fitted actor-critic network (BPNN-ACN-PID). Finally, the effectiveness of the proposed method is validated through control experiments using actual process data from a MSWI plant in Beijing. © 2024 IEEE.

关键词:

Municipal solid waste Reinforcement learning Proportional control systems Process control Adaptive control systems Three term control systems Furnaces Waste incineration Temperature Two term control systems

作者机构:

  • [ 1 ] [Tian, Hao]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
  • [ 2 ] [Tang, Jian]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
  • [ 3 ] [Xia, Heng]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
  • [ 4 ] [Wang, Tianzheng]Beijing University Of Technology, Faculty Of Information Technology, Beijing, China
  • [ 5 ] [Yu, Wen]CINVESTAV-IPN, Departamento De Control Automatico, Mexico City, Mexico
  • [ 6 ] [Wu, Zhiwei]Northeastern University, State Key Laboratory Of Synthetical Automation For Process Industries, Shenyang, China

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年份: 2024

页码: 1957-1961

语种: 英文

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