A Multistep Sequence-to-Sequence Model With Attention LSTM Neural Networks for Industrial Soft Sensor Application

Soft sensor technology is widely used in industries to handle highly nonlinear, dynamic, time-dependent sequence data of industrial processes for predicting the key variables associated with auxiliary process variables. Many existing soft sensor algorithms based on deep learning are able to build complex nonlinear models but ignore the dynamic characteristics of processes. The long short-term memory (LSTM) neural network is exploited to solve the modeling issue, which is related to strong time-varying features. In this article, a novel multistep sequence-to-sequence model based on attention LSTM (MA-LSTM) neural networks is proposed to improve the soft sensor modeling performance of industrial processes with strong dynamics and nonlinearity. The LSTM-based encoder–decoder architecture with the attention mechanism is applied to extract inherent characteristics relevant to the quality variables and capture both the long- and short-term dependences of sequence data. Instead of the traditional sequence-to-point quality prediction architecture, the improved architecture is designed to predict multistep quality variables, and the intermediate results are fed into an additional 1-D weighted convolution module to obtain accurate prediction results. The superiority of the proposed framework is demonstrated through a debutanizer column case and a sulfur recovery process.