Novel Stacked Input-Enhanced Supervised Autoencoder Integrated With Gated Recurrent Unit for Soft Sensing

Machine learning techniques have been successfully utilized as effective soft sensing for industrial processes. Unfortunately, as modern industrial processes become increasingly complex in the terms of scale and integration, the collected process data become more complicated in terms of dimensionality. Thus, it becomes a great challenge to establish an accurate soft sensor model for dynamic processes. In this article, a novel stacked input-enhanced supervised autoencoder (SISAE) integrated with gated recurrent unit (GRU) is proposed. In SISAE-GRU, the presented SISAE model is used to extract high-level and useful features from collected high-dimensionality process data. Different from the standard stacked autoencoder (SAE) model, the original input variables are embedded into each autoencoder (AE) unit of the presented SISAE model during the pretraining process by supervised learning, enhancing the feature extraction performance of SAE. Considering the dynamic nature between extracted features and output variables, GRU is utilized to learn the dynamic relationships and a dynamic model of soft sensing is finally constructed using a fully connected (FC) layer. To illustrate the effectiveness and superiority of the proposed SISAE-GRU model, two industrial datasets from the debutanizer column and the purified terephthalic acid (PTA) process are utilized. The experimental results show that the proposed SISAE-GRU can reduce the root-mean-squared error (RMSE) by 53.6% and 46.1% on average compared with some other state-of-the-art methods for the debutanizer column and PTA, respectively.