Development of Robust and Physically Interpretable Soft Sensor for Industrial Distillation Column Using Transfer Learning with Small Datasets

Yu-Da Hsiao,David Shan-Hill Wong,Jia-Lin Kang

doi:10.3390/pr9040667

Yu-Da Hsiao, David Shan-Hill Wong + Show 1 more

Open Access

https://doi.org/10.3390/pr9040667

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Abstract

In the development of soft sensors for industrial processes, the availability of data for data-driven modeling is usually limited, which led to overfitting and lack of interpretability when conventional deep learning models were used. In this study, the proposed soft sensor development methodology combining first-principle simulations and transfer learning was used to address these problems. Source-domain models were obtained using a large amount of data generated by dynamic simulations. They were then fine-tuned by a limited amount of real plant data to improve their prediction accuracies on the target domain and guaranteed the models with correct domain knowledge. An industrial C4 separation column operating at a refining unit was used as an example to illustrate the effectiveness of this approach. Results showed that fine-tuned networks could obtain better accuracy and improved interpretability compared to a simple feedforward network with or without regularization, especially when the amount of actual data available was small. For some secondary effects, such as interaction gain, its interpretability is mainly based on the interpretability of the corresponding source models.

Highlights

Soft sensors are virtual sensors instantly estimating hard-to-measure variables, such as concentration, which is traditionally measured online by low frequency laboratory analysis through inputting easy-to-measure variables, such as pressure, temperature, and flowrate
The model-driven soft sensors were commonly based upon first-principle models, while the data-driven ones were usually based on regression techniques such as principal component analysis, partial least squares, neuro-fuzzy systems, support vector machines, and artificial neural networks (ANNs)
With the advanced progressions in deep learning, ANN variants once again caught the attention of process engineers due to their power in nonlinear regression ability

Summary

Introduction

Soft sensors are virtual sensors instantly estimating hard-to-measure variables, such as concentration, which is traditionally measured online by low frequency laboratory analysis through inputting easy-to-measure variables, such as pressure, temperature, and flowrate. The model-driven soft sensors were commonly based upon first-principle models, while the data-driven ones were usually based on regression techniques such as principal component analysis, partial least squares, neuro-fuzzy systems, support vector machines, and artificial neural networks (ANNs). The ANN variants were black boxes, usually difficult to interpret by domain knowledge [1] Such a drawback held scientists and engineers from further implementing ANNs on the systems they were focusing on, slowing down their popularizing rates. With these concerns, explainable artificial intelligence (AI), which aims to make AI interpretable and trustworthy, became a focusing field of machine learning [2]. For process engineering and control, it was critical and necessary to implement interpretable models into the processes to make sure the predictions of these models were not merely accurate and interpretable based on domain knowledge

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