基于 KPCA 和 BiLSTM 的分解炉出口温度预测

水泥生产过程中,分解炉出口温度是非常重要的工艺参数,为了应对出口温度变量的多样性,文章提出一种核主成分分析(kernel principal component analysis,KPCA)与双向长短期记忆(bidirectional long short-term memory,BiLSTM)神经网络相结合的温度预测组合模型用来预测分解炉的出口温度。通过KPCA筛选出影响因素的主成分从而达到数据降维目的,将降维后的主成分作为BiLSTM神经网络的输入,分解炉出口温度作为BiLSTM神经网络的输出。经BiLSTM神经网络训练,得到分解炉出口温度预测模型。通过对比验证表明,使用KPCA-BiLSTM相结合的温度预测模型具有较好的预测精度。

In the cement production process, the outlet temperature of the calciner is a very important process parameter. In order to cope with the diversity of outlet temperature variables, this paper proposes a combined model of temperature prediction using kernel principal component analysis (KPCA) and bidirectional long short-term memory (BiLSTM) neural network to predict the outlet temperature of the calciner. Through the KPCA, the main components that affect the temperature variables at the outlet of the calciner are selected to achieve the data dimensionality reduction. The reduced main components are used as the input of BiLSTM, and the temperature at the outlet of the calciner is used as the output of BiLSTM. After BiLSTM neural network training, the outlet temperature prediction model of the calciner is obtained. The comparison and verification show that the temperature prediction model with KPCA-BiLSTM has better prediction accuracy.