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

Abstract

Soft sensor technology is widely used in the 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 paper, a novel multi-step 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-term and short-term dependences of sequence data. Instead of the traditional sequence-to-point quality prediction architecture, the improved architecture is designed to predict multi-step quality variables, and the intermediate results are fed into an additional one-dimensional 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.