BP神经网络在垃圾焚烧电厂酸性气体排放控制中的应用  

Application of BP Neural Network in Acid Gas Emission Control of Waste Incineration Power Plant

作　　者：游敏 YOU Min(Xianyou Xinghong Environment Protection Power Co.,Ltd.,Putian,Fujian 351261,China)

机构地区：[1]仙游兴鸿环保电力有限公司,福建莆田351261

出　　处：《自动化应用》2024年第1期35-37,共3页Automation Application

摘　　要：对于采用半干法的大多数垃圾焚烧发电厂,控制酸性气体排放浓度的主要手段是调节脱酸塔入口石灰浆流量。传统的PID控制采用CEMS实测数据作为反馈信号调节石灰浆流量,但脱酸塔入口到烟囱烟气采样点距离远,反应时间长,控制效果不佳。为此,引入BP人工神经网络遗传算法,并利用人工控制模式下积累的大量过程数据训练人工神经网络,以替代人工控制。For most waste incineration power plants that use semi dry methods,the main means of controlling the concentration of acidic gas emissions is to regulate the lime slurry flow rate at the inlet of the deacidification tower.Traditional PID control uses measured data from CEMS as feedback signal to regulate the lime slurry flow,but the distance from the inlet of the deacidification tower to the sampling point of the chimney flue gas is far,the reaction time is long,and the control effect is poor.To this end,the BP artificial neural network genetic algorithm is introduced to train the artificial neural network using a large amount of process data accumulated under manual control mode,in order to replace manual control.

关 键 词：大滞后 串级控制 前馈控制 BP神经网络 遗传算法 大数据 

分 类 号：X701[环境科学与工程—环境工程]