基于LSSA优化的火电机组脱硫系统PID-GPC复合控制研究

何佳玮; 康英伟

doi:10.19805/j.cnki.jcspe.2025.240439

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基于LSSA优化的火电机组脱硫系统PID-GPC复合控制研究

数字化与智能化 | 更新时间：2025-11-28

- 基于LSSA优化的火电机组脱硫系统PID-GPC复合控制研究
- Study on PID-GPC Composite Control of Desulfurization System in Thermal Power Plant Based on LSSA Optimization
- 动力工程学报 2025年45卷第9期页码：1492-1501
- 作者机构：
  
  上海电力大学自动化工程学院,上海,200090
- 作者简介：
  
  [ "何佳玮(1999—),男,上海人,硕士研究生,研究方向为先进发电过程的建模、仿真与控制" ]
  [ "康英伟(通信作者),男,副教授,博士,E-mail:controlkyw@126.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(61573239);上海发电过程智能管控工程技术研究中心资助项目(14DZ2251100)
- DOI：10.19805/j.cnki.jcspe.2025.240439
  中图分类号：
- 网络出版：2025-09-16，
  
  纸质出版：2025
- 稿件说明：
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何佳玮,康英伟. 基于LSSA优化的火电机组脱硫系统PID-GPC复合控制研究动力工程学报, 2025, 45(9): 1492-1501 https://doi.

org/10.19805/j.cnki.jcspe.2025.240439
何佳玮,康英伟. 基于LSSA优化的火电机组脱硫系统PID-GPC复合控制研究动力工程学报, 2025, 45(9): 1492-1501 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240439.

org/10.19805/j.cnki.jcspe.2025.240439 DOI：

摘要

为提高燃煤火力发电厂石灰石-石膏湿法脱硫效率

同时保证电厂工作运行的稳定

提出一种基于比例-积分-微分(proportional-integral-derivative

PID)与广义预测控制(generalized predictive control

GPC)加权的复合控制方案

以确保电厂脱硫塔浆液pH值和出口SO

质量浓度处于理想值

从而实现合理的脱硫控制目标。其中

脱硫系统模型参数、PID参数、PID-GPC复合控制权重因子均利用改进麻雀搜索算法(logistic sparrow search algorithm

LSSA)进行寻优。仿真结果表明:辨识模型能作为GPC的预测模型;复合控制算法融合了PID与GPC的优点

系统的响应特性得到提升;LSSA优化后的复合控制动态性能得到提升

抗干扰能力明显增强

同时能够有效跟踪设定目标。

Abstract

To improve the efficiency of limestone-gypsum wet flue gas desulfurization in coal-fired power plants while ensuring stable operation

a weighted composite control scheme based on proportional-integral-derivative (PID) and generalized predictive control (GPC) was proposed for maintaining the pH value of the desulfurization tower slurry and the outlet SO

mass concentration at ideal levels

thereby achieving a reasonable desulfurization control target. The desulfurization system model parameters

PID parameters

and the weight factors of the PID-GPC composite control were all optimized using an improved logistic sparrow search algorithm (LSSA). Simulation results ind

icate that the identified model can serve as the prediction model for GPC; the composite control algorithm integrates the advantages of both PID and GPC

enhancing the system's response characteristics; and the composite control optimized by LSSA exhibits improved dynamic performance

significantly enhances anti-interference capability

and effectively tracks the set targets.

关键词

Keywords

references

郑仙荣, 梁云波, 谭煜幺. 高铁灰对钙基脱硫剂脱除SO2行为的影响[J]. 煤炭学报, 2022, 47(增刊1): 350-355. ZHENG Xianrong, LIANG Yunbo, TAN Yuyao. Effect of high-content iron ash on removal of SO2 by calcium-based desulfurizer[J]. Journal of China Coal Society, 2022, 47(Sup1): 350-355.

SHIGEMORI H. Desulphurization control system through locally weighted regression model[J]. IFAC Proceedings Volumes, 2012, 45(23): 234-239.

LIU Shan, ZHONG Wenqi, SUN Li. Uncertainty and disturbance estimator-based model predictive control for wet flue gas desulphurization system[J]. Chinese Journal of Chemical Engineering, 2024, 67: 182-194.

齐亚兵, 唐承卓, 贾宏磊. 工业烟气湿法脱硫技术的发展现状及研究新进展[J]. 材料导报, 2022, 36(增刊1): 88-96. QI Yabing, TANG Chengzhuo, JIA Honglei. Development status and new research progress of wet desulfurization technologies for industrial flue gas[J]. Materials Reports, 2022, 36(Sup1): 88-96.

邱韬, 王瑞民, 徐防, 等. 湿法脱硫系统浆液ORP参数模型与氧化控制研究[J]. 热力发电, 2022, 51(7): 103-109. QIU Tao, WANG Ruimin, XU Fang, et al. Study on slurry ORP parameters model and oxidation control in wet desulphurization systems[J]. Thermal Power Generation, 2022, 51(7): 103-109.

田森浩, 向勇林, 陈冬林, 等. 内置导流板的湿法脱硫塔数值模拟优化及应用[J]. 动力工程学报, 2021, 41(9): 766-772, 785. TIAN Senhao, XIANG Yonglin, CHEN Donglin, et al. Numerical simulation optimization and application of wet desulfurization tower with inner guide plate[J]. Journal of Chinese Society of Power Engineering, 2021, 41(9): 766-772, 785.

侯建勇, 严芳, 王浩, 等. 基于因果-模糊层次分析的湿法脱硫系统运行稳定性综合评价[J]. 化工进展, 2022, 41(2): 569-583. HOU Jianyong, YAN Fang, WANG Hao, et al. Comprehensive evaluation of operation stability of wet desulfurization system based on causal-fuzzy AHP[J]. Chemical Industry and Engineering Progress, 2022, 41(2): 569-583.

侯雪峰, 张九根, 梁星. 粒子群优化神经网络PID控制在湿法烟气脱硫中的应用[J]. 煤炭技术, 2018, 37(4): 327-330. HOU Xuefeng, ZHANG Jiugen, LIANG Xing. Particle swarm optimization PID neural network application in wet flue gas desulfurization[J]. Coal Technology, 2018, 37(4): 327-330.

CARLETTI C, DE BLASIO B, MKIL E, et al. Optimization of a wet flue gas desulfurization scrubber through mathematical modeling of limestone dissolution experiments[J]. Industrial & Engineering Chemistry Research, 2015, 54(40): 9783-9797.

ZHAO Zhongyang, LI Qinwu, SHAO Yuhao, et al. Prediction of inlet SO2 concentration of wet flue gas desulfurization (WFGD) by operation parameters of coal-fired boiler[J]. Environmental Science and Pollution Research, 2023, 30(18): 53089-53102.

马双忱, 周权, 曹建宗, 等. 湿法脱硫系统动态过程建模与仿真[J]. 化工学报, 2020, 71(8): 3741-3751. MA Shuangchen, ZHOU Quan, CAO Jianzong, et al. Modeling and simulation of wet desulfurization system dynamic process[J]. CIESC Journal, 2020, 71(8): 3741-3751.

尚慧. 300 MW 机组湿式石灰石/石膏烟气脱硫实时仿真系统研究[D]. 保定: 华北电力大学, 2010.

王超, 康英伟. 基于SRIVC算法的石灰石-石膏烟气湿法脱硫系统模型辨识[J]. 锅炉技术, 2023, 54(3): 75-79. WANG Chao, KANG Yingwei. Model identification of limestone-gypsum wet flue gas desulfurization system based on SRIVC algorithm[J]. Boiler Technology, 2023, 54(3): 75-79.

康英伟, 孙智滨, 常俊. 基于改进差分进化算法的湿法烟气脱硫系统模型辨识[J]. 动力工程学报, 2019, 39(10): 834-839. KANG Yingwei, SUN Zhibin, CHANG Jun. Model identification for a wet desulfurization system based on improved differential evolution algorithm[J]. Journal of Chinese Society of Power Engineering, 2019, 39(10): 834-839.

CHEN Zhuo, HAO Yongsheng, SU Zhigang, et al. Predictor-based active disturbance rejection control of wet flue gas desulfurization system with delay robustness and simplified tuning[J]. IEEE Transactions on Automation Science and Engineering, 2024, 22: 3731-3742.

张璐, 钟文琪, 李益国, 等. 面向DCS优化控制的氨法脱硫过程模拟[J]. 东南大学学报(自然科学版), 2018, 48(3): 435-442. ZHANG Lu, ZHONG Wenqi, LI Yiguo, et al. Simulation of ammonia desulfurization process for DCS optimization control[J]. Journal of Southeast University (Natural Science Edition), 2018, 48(3): 435-442.

薛生辉, 曲俊海, 王永宏, 等. 比例-积分控制加广义预测控制算法及其应用[J]. 控制理论与应用, 2018, 35(9): 1320-1330. XUE Shenghui, QU Junhai, WANG Yonghong, et al. Proportional-integral control plus generalized predictive control algorithm and its application[J]. Control Theory & Applications, 2018, 35(9): 1320-1330.

DE B ARAU＇JO R, COELHO A A R. Hybridization of IMC and PID control structures based on filtered GPC using genetic algorithm[J]. Computational and Applied Mathematics, 2018, 37(2): 2152-2165.

赵征, 马毅杰. 基于多模型γ增量型阶梯式GPC的SCR脱硝系统优化控制研究[J]. 动力工程学报, 2023, 43(6): 735-741. ZHAO Zheng, MA Yijie. Research on optimal control of SCR denitrification system based on multi-model γ incremental stepped GPC[J]. Journal of Chinese Society of Power Engineering, 2023, 43(6): 735-741.

XU Min, LI Shaoyuan, CAI Wenjian, et al. Effects of a GPC-PID control strategy with hierarchical structure for a cooling coil unit[J].Energy Conversion and Management, 2006, 47(1): 132-145.

XUE Jiankai, SHEN Bo. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8(1): 22-34.

周屈兰, 徐通模, 惠世恩. 我国自主开发的湿法脱硫技术及其应用[J]. 动力工程, 2006, 26(2): 261-266, 272. ZHOU Qulan, XU Tongmo, HUI Shien. Development and application of China's own wet flue gas desulfurization technique[J]. Journal of Power Engineering, 2006, 26(2): 261-266, 272.

许景辉, 王雷, 谭小强, 等. 基于SOA优化PID控制参数的智能灌溉控制策略研究[J]. 农业机械学报, 2020, 51(4): 261-267. XU Jinghui, WANG Lei, TAN Xiaoqiang, et al. Application of PID control based on SOA optimization in intelligent irrigation system[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(4): 261-267.

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