摘要
针对燃煤电厂湿式石灰石-石膏湿法烟气脱硫(WFGD)过程中浆液pH值测量时间长,不利于WFGD作业的问题,建立高精度的浆液pH值模型。基于深度学习的框架,利用长短期记忆神经网络(LSTM)算法对时间序列处理上的优越性进行建模,该模型具有良好的精确度和泛化能力。将燃煤机组实际运行数据中与浆液pH值变化相关的变量作为模型的辅助变量,建立基于LSTM神经网络的浆液pH值预测模型。对模型进行仿真验证,并分别与BP神经网络模型和最小二乘支持向量机(LSSVM)模型比较,结果表明LSTM神经网络模型的预测精度最高,验证了LSTM神经网络在工业建模中的优良性能。
Aiming at the problem that the measurement time of slurry pH in wet limestone-gypsum wet flue gas desulfurization(WFGD)process in coal-fired power plants is long,which is not conducive to WFGD operation,a high-precision slurry pH model was established.Therefore,based on the framework of deep learning,the long-term and short-term memory neural network(LSTM)algorithm was used to model the superiority of time series processing.The model has good accuracy and generalization ability.The slurry pH prediction model based on LSTM neural network was established by using variables related to slurry pH changes in actual operating data of coal-fired units as the auxiliary variables of the model.The model was simulated and verified,and compared with the BP neural network model and the least square support vector machine(LSSVM)model.The results show that the LSTM neural network model has the highest prediction accuracy,which verifies the excellent performance of the LSTM neural network in industrial modeling.
作者
金秀章
景昊
Jin Xiuzhang;Jing Hao(School of Control and Computer Engineering,North China Electric Power University,Baoding 071003,China)
出处
《信息技术与网络安全》
2020年第8期62-66,共5页
Information Technology and Network Security
