能源工程2024，Vol.44Issue(4)：69-77,9.DOI:10.16189/j.nygc.2024.04.012
基于LSTM预测反馈的垃圾焚烧烟气湿法脱酸控制优化
Optimization of wet deacidification control of waste incineration flue gas based on LSTM prediction feedback
摘要
Abstract
Absorbent flow control is key part in wet deacidification system control.A predictive method on the control of absorption liquid flow based on LSTM neural network algorithm was proposed to suppress the unstableness in flow control caused by long pH feedback delay in existing control systems.The concentrations of SO2 at the inlet and outlet of deacidification system were used as feedforward signal for absorption liquid flow control.By modeling and analysis of actual operation data,the error between predicted flow rate of absorption liquid and actual value under stable load change and the concentration of sulfur dioxide are within 4.5%～6.3%,and the predicted flow of absorption liquid is close to original flow,which meets the demand of stable discharge of outlet SO2 gas concentration.Under unstable load conditions,the absorption liquid flow rate increased by an average of 1.7%compared with original flow control method,the fluctuation range of outlet sulfur dioxide concentration value decreased by 23.4%,the total sulfur dioxide emission decreased by 8.77%and the stability and efficiency of desulfurization were significantly improved.关键词
湿法脱酸/吸收液流量/LSTM网络/预测/控制Key words
wet deacidification/absorption fluid flow/LSTM network/forecast/control分类
通用工业技术引用本文复制引用
罗国鹏,胡思捷,孙殿伟,黄群星,高峰,朱燕华,时丕伟,王松,汪守康..基于LSTM预测反馈的垃圾焚烧烟气湿法脱酸控制优化[J].能源工程,2024,44(4):69-77,9.基金项目
生活垃圾焚烧炉全过程高效清洁焚烧智慧控制系统(FYNY-HT-20220314) （FYNY-HT-20220314）
中央高校基本科研业务费专项资金资助(2022ZFJH04) （2022ZFJH04）
