摘要
人工智能技术的不断发展,为定量分析脱硫系统各因素变化带来的影响,从而预测系统脱硫效率和经济成本创造了条件。分析了近年来利用神经网络算法预测脱硫系统SO2排放量的算法网络结构、输入参数选择等算法细节,并讨论了神经网络算法应用于脱硫系统的特点。此外,针对基于脱硫系统设计的预测+优化算法的设计细节和优化目标,对增压风机、氧化风机和浆液循环泵的运行优化做了针对性讨论。
The continuous development of artificial intelligence technology has impacted quantitative analysis flue gas desulfurization(FGD)system in various factors,and has created favorable condition for its desulfurization efficiency and economic cost.By analyzing the details of the network construction and parameter selection in FGD system SO2 emission prediction calculated by neural network algorithm,the application characteristics of the algorithm in FGD system is discussed.In addition,targeted discuss is made on the optimization of booster fan,oxidation fan and syrup circulation pump,which aims at refining the detailed design and optimization targets of the prediction+optimization algorithm in FGD systems.
作者
曹建宗
林宸雨
陈文通
刘琦
周权
要亚坤
樊帅军
马彩妮
马双忱
CAO Jianzong;LIN Chenyu;CHEN Wentong;LIU Qi;ZHOU Quan;YAO Yakun;FAN Shuaijun;MA Caini;MA Shuangchen(Shenzhen Energy Baoding Power Company Limited,Baoding 072150,China;Department of Environmental Science and Engineering,North China Electric Power University,Baoding 071003,China)
出处
《华电技术》
CAS
2020年第3期59-66,共8页
HUADIAN TECHNOLOGY
关键词
脱硫系统
SO2排放量
预测
神经网络
优化
遗传算法
粒子群优化算法
智慧环保
人工智能
flue gas desulfurization
SO2 emission
prediction
neural network
optimization
genetic algorithm
particle swarm optimization
intelligent environmental protection
artificial intelligence
