Abstract
This paper presents developing soft sensors for polymer melt index in an industrial polymerization process by using deep belief network (DBN). The important quality variable melt index of polypropylene is hard to measure in industrial processes. Lack of online measurement instruments becomes a problem in polymer quality control. One effective solution is to use soft sensors to estimate the quality variables from process data. In recent years, deep learning has achieved many successful applications in image classification and speech recognition. DBN as one novel technique has strong generalization capability to model complex dynamic processes due to its deep architecture. It can meet the demand of modelling accuracy when applied to actual processes. Compared to the conventional neural networks, the training of DBN contains a supervised training phase and an unsupervised training phase. To mine the valuable information from process data, DBN can be trained by the process data without existing labels in an unsupervised training phase to improve the performance of estimation. Selection of DBN structure is investigated in the paper. The modelling results achieved by DBN and feedforward neural networks are compared in this paper. It is shown that the DBN models give very accurate estimations of the polymer melt index.
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Acknowledgments
The work was supported by National Natural Science Foundation of China (No. 61673236) and the European Union (No. PIRSES-GA-2013-612230).
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Chang-Hao Zhu received the B. Sc. degree in mechanical engineering from Shandong University, China in 2014, and the M. Sc. degree in electrical power from Newcastle University, UK in 2016. Currently, he is a Ph. D. degree candidate in chemical engineering at the School of Engineering, Newcastle University, UK.
His research interests include process control, machine learning, development of data driven soft sensor and their applications industrial chemical processes.
Jie Zhang received the B. Sc. degree in control engineering from Hebei University of Technology, China in 1986, and the Ph. D. degree in control engineering from City University, UK in 1991. He is a Reader in the School of Engineering, Newcastle University, UK. He has published over 300 papers in international journals, books and conferences. He is a senior member of IEEE, a member of the IEEE Control Systems Society, IEEE Computational Intelligence Society, and IEEE Industrial Electronics Society. He is on the Editorial Boards of a number of journals including Neurocomputing published by Elsevier.
His research interests are in the general areas of process system engineering including process modelling, batch process control, process monitoring, and computational intelligence.
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Zhu, CH., Zhang, J. Developing Soft Sensors for Polymer Melt Index in an Industrial Polymerization Process Using Deep Belief Networks. Int. J. Autom. Comput. 17, 44–54 (2020). https://doi.org/10.1007/s11633-019-1203-x
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DOI: https://doi.org/10.1007/s11633-019-1203-x