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Author:

Wang, Tianzheng (Wang, Tianzheng.) | Tang, Jian (Tang, Jian.) | Xia, Heng (Xia, Heng.)

Indexed by:

CPCI-S EI

Abstract:

The automatic combustion control (ACC) system of municipal solid waste incineration (MSWI) processes realizes the stable operation through the coupling control among multiple loops. However, due to the difference in municipal waste solid components and operating level, the introduced ACC system is difficult to be directly applied in China. The research on MSWI process controlling is still the key to solve its stable running. Aiming at the strong coupling and nonlinear characteristics of MSWI processes, this paper proposes a multiple input multiple output control method based on single neuron adaptive PID. Firstly, multi-input multi-output controlled object models in terms of furnace temperature (FT), boiler steam flow (BSF), and oxygen (OX) content are established, whose input and output are determined on the basis of MSWI process flow and ACC system's mechanism. Then, based on the experience of experts in the field, the multi-loop controller of the MSWI process is established by using single neuron adaptive PID controller. Finally, the accuracy and effectiveness of the proposed framework are verified by actual industrial process data. © 2023 IEEE.

Keyword:

Adaptive control systems Municipal solid waste Waste treatment MIMO systems Three term control systems Waste incineration Neurons Process control Controllers Boilers Feedback control

Author Community:

  • [ 1 ] [Wang, Tianzheng]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

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Source :

Year: 2023

Page: 331-336

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 6

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