基于Smith预估模糊PID的氨法脱硫控制策略

doi:10.16180/j.cnki.issn1007-7820.2023.05.012

Abstract

Abstract:

The ammonia desulfurization system has the problems of large lag, nonlinearity and poor real-time load tracking. Smith predictive controller has been designed to solve these problems, which improves the real-time performance of the system by compensating the delay time. However, the PID parameters of system are always set by cut-and-try method, which results in poor stability of the system. In this study, a fuzzy PID parameters adaptive adjustment method is proposed. The adjustment values of parameters of the PID controller are obtained by the fuzzy algorithm, and the PID parameters are adjusted adaptively, so that the SNOx concentration is controlled near the preset value. Different from the traditional step signal judgment control effect, the proposed method uses real-time load data to simulate the model, and the data simulation results prove the feasibility of Smith calculation fuzzy PID controller. When the system is stable, the error between SNOx concentration and the preset value is within 0.5 ppm, and adjustment time is greatly shortened. These results show that the proposed method improves the real-time tracking performance of the ammonia desulfurization control system, and realizes the fast, accurate and stable desulfurization control performance.

Key words: ammonia desulfurization, Smith predictor, fuzzy algorithm, delay time, real tracking control, PID parameter tuning, desulfurization system, pure lag link

CLC Number:

TP273

ZHU Mingji,ZHANG Huilin,MA Lixin. Control Strategy of Ammonia Desulfurization Based on Smith Predictive Fuzzy PID[J].Electronic Science and Technology, 2023, 36(5): 80-87.

Figures/Tables 12

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References 20

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控制方法	上升时间 /s	峰值时间 /s	稳定时平均波动范围/ppm
传统PID控制	131.0	152.0	大于4.0
Smith预估器控制	125.0	147.8	2.3
Smith预估模糊PID控制	41.6	52.0	0.5

Control Strategy of Ammonia Desulfurization Based on Smith Predictive Fuzzy PID

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References 20

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[1]	LI Meng,MA Lixin. Research on Predictive Ammonia Desulfurization Control System [J]. Electronic Science and Technology, 2022, 35(5): 60-65.
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[3]	WU Qiang,ZHANG Wei,YANG Huiting,WANG Chaoying. Application of Particle Swarm Optimization Based on Beetle Antennae Search Algorithm in PID Parameter Tuning [J]. Electronic Science and Technology, 2020, 33(6): 18-23.
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[5]	YIN Hong-Jian. Research into the Queue Scheduling Algorithm Based on Multi-service [J]. , 2010, 23(5): 96-.

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