化工过程数据的动态性和非线性等特性常使传统的软测量方法难以准确提取数据的动态和非线性特征,从而影响关键质量变量的预测精度和系统整体的控制优化.因此,提出了一种融合多头自注意力机制的长短期记忆网络(multi-head self-attention mechanism long short-term memory network,MHSA-LSTM)的软测量建模方法.首先,利用LSTM充分挖掘数据的时序特征,以便提取化工过程数据的动态变化信息;其次,使用多头自注意力机制对LSTM隐藏层的输出特征进行加权,可有效地捕捉不同尺度特征向量的长期相关性,且能提高模型的长期记忆能力;然后,将提取的特征向量与其对应的特征权重相乘得出加权结果输入全连接层,可有效地提高关键质量变量预测的精度.对所提方法在脱丁烷塔过程和硫回收单元进行仿真验证,结果表明所建模型的预测精度优于门控循环单元、LSTM以及融合自注意力机制的LSTM软测量模型.
Soft sensor modeling based on MHSA-LSTM and its application in chemical process
The dynamic and nonlinear characteristics of chemical process in data often make it difficult even impossible for traditional soft sensing methods to accurately extract the dynamics and nonlinearity,which affects the prediction accuracy of key quality variables negatively and the overall control optimization of the system.Therefore,this paper proposes a soft sensor model,termed as the multi-head self-attention mechanism long short-term memory network(MHSA-LSTM).First,the LSTM is used to fully exploit the temporal characteristics of the data in order to extract the dynamic change information of the chemical process data.Second,a multi-head self-attention mechanism is used to weight the output features of LSTM hidden layer,and effectively capture the long-term correlation of feature vectors with different scales and improve the long-term memory ability of the model.Furthermore,the weighted results obtained by multiplying the extracted feature vector and its corresponding feature weight are input to the full connection layer.It can effectively improve the accuracy of prediction of key quality variables.Finally,the proposed method is simulated and verified in the debutanizer column process and sulfur recovery unit.The results indicate that the prediction accuracy of the constructed model is superior to gated recurrent unit,LSTM and self-attention LSTM soft sensing models.
万方数据
维普
