中图分类号：TK39；TP183
文献标识码：A
DOI：10.19358/j.issn.2096-5133.2020.08.012
引用格式： 金秀章，景昊. 基于LSTM的湿法烟气脱硫浆液pH值建模[J].信息技术与网络安全，2020，39(8)：62-66.

Modeling of pH value of wet flue gas desulfurization slurry based on LSTM

Abstract：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.

Key words :slurry pH prediction；long and short-term memory network(LSTM)；wet limestone-gypsum wet flue gas desulfurization(WFGD)；time series