A weighted auto regressive LSTM based approach for chemical processes modeling

Abstract

Data-driven methods have been regarded as effective methods for modeling in chemical processes. However, with the increasing complexity of chemical processes in spatial domain and time domain, how to extract meaningful features and build corresponding models are keys for accurate modeling tasks. To retain temporal features of original inputs, a Recurrent Denosing Autoencoder (RDAE) is built to extract meaningful features and reduce input dimension in the spatial domain, and Cumulative Percent Variance (CPV) is introduced to decide the number of extracted features. Considering correction and prior knowledge effects of real history outputs, a Weighted Auto Regressive Long Short Term Memory (WAR-LSTM) structure is proposed as the basic cell, then multiple WAR-LSTMs are stacked as deep WAR-LSTM to extract high level representations from multi variables. Hence, spatial and temporal information are sufficiently used both in extracting features and building model. The benchmarked Tennessee Eastman process data and real process data from a Fluid Catalytic Cracking (FCC) unit verify the effectiveness of our work.