基于变量选择和EMD-LSTM网络的出口SO_(2)浓度预测

Prediction of Outlet SO_(2) Concentration Based on Variable Selection and EMD-LSTM Network

针对燃煤电厂脱硫系统出口SO_(2)浓度难以稳定控制的问题,该文提出一种基于变量选择和经验模态分解(empirical mode decomposition,EMD)–长短期记忆网络(long short-term memory,LSTM)的预测模型。首先,通过机理分析确定与出口SO_(2)有关的相关变量,利用套索(least absolute shrinkage and selection operator,LASSO)算法去除冗余变量。利用互信息确定各输入变量与输出变量之间的时间延迟,并进行时延补偿。对补偿后的数据通过EMD算法进行分解,并作为最终的输入变量。利用LSTM建立出口SO_(2)浓度的预测模型。仿真实验表明,LASSO算法去除了冗余变量,提高了模型泛化能力;EMD分解能够提取数据中的有效信息,降低了模型预测误差;利用LSTM建立的模型其预测精度最高,能够准确预测出口SO_(2)浓度变化。所得结果对实现脱硫系统稳定运行具有重要意义。

Aiming at the problem that it is difficult to stably control the SO_(2)concentration at the outlet of the desulfurization system in a coal-fired power plant, a prediction model based on variable selection and empirical mode decomposition(EMD)-long short-term memory network(LSTM) was proposed. First, the relevant variables related to outlet SO_(2)were determined through mechanism analysis, and the LASSO algorithm was used to remove the redundant variables. Mutual information was used to determine the time delay between input variables and output variables, and time delay compensation was carried out. The compensated data was decomposed by EMD algorithm and used as the final input variable. The prediction model of SO_(2)concentration at the outlet was established by using LSTM. Simulation results show that Lasso algorithm removes redundant variables and improves the generalization ability of the model;EMD decomposition can extract effective information from the data and reduce the prediction error of the model;the model established by LSTM has the highest prediction accuracy and can accurately predict the change of SO_(2)concentration at the outlet, which is of great significance to realize the stable operation of desulfurization system.