摘要
通过研究干粉煤气化技术的工艺流程,确定了影响气化炉煤粉质量流量的主要工艺参数,采用BP神经网络构造煤粉质量流量软测量模型,通过大量历史数据对该模型进行训练和检验,实现了对气化炉煤粉质量流量的实时准确测量。现场应用结果表明,基于神经网络的煤粉质量流量测量模型,测量结果可靠,精度较高,应用性强。
Selected the main process parameters which influence the casifier pulverized-coal mass flow by researching the process of dry pulverized-coal gasification technology.Established soft sensor model of mass flow based on BP neural network.Trained and verified the model using the large amounts of history data.Realized the on-line real-time measurement of gasifier pulverized-coal mass flow.The results on site show that the measurement model of casifier pulverized-coal mass flow based on neural network is credible,highly precise and strongly applicable.
作者
何吉坤
许力强
王俊杰
He Ji-kun;Xu Li-qiang;Wang Jun-jie
出处
《化工设计通讯》
CAS
2018年第11期10-10,93,共2页
Chemical Engineering Design Communications
关键词
神经网络
煤粉
质量流量
气化炉
neural network
pulverized-coal
mass flow
gasifier
