(259g) Development of Data-Driven and Hybrid Models for Continuous Pharmaceutical Manufacturing Lines Under Industry 4.0 Framework
- Conference: AIChE Annual Meeting
- Year: 2019
- Proceeding: 2019 AIChE Annual Meeting
- Group: Pharmaceutical Discovery, Development and Manufacturing Forum
- Time: Tuesday, November 12, 2019 - 10:06am-10:27am
As one of the three main routes of continuous pharmaceutical manufacturing, the continuous direct compaction (DC) line has mechanistic and semi-empirical models developed2, 11. In this study, an integrated data collection and data analysis framework is constructed for the DC line using Industry 4.0 concepts to facilitate the development of data-driven and hybrid models. After the implementation of the Industry 4.0 framework, both process and analytical data of the DC line can be centralized into a cloud-based platform, where users can retrieve and visualize all relevant manufacturing information in the system. With a large amount of data collected, various machine learning techniques like Support Vector Machine (SVM) and Artificial Neural Network (ANN) are applied to infer data-driven models of each unit operation and the entire flowsheet. The data-driven models developed can then be used as a gap-assessment tool to improve the previously developed mechanistic and semi-empirical models, leading to hybrid models. The wealth of data in the system are also used to test the applicability of both types of models. The standalone data-driven models can provide quick input-output correspondence in the absence of mechanistic models, but on top of it, the hybrid models can offer a layer of system understanding. The highlight of this work is to apply a data-driven approach to supplement the mechanistic models and to form hybrid models, which can be used to improve the predictability of the continuous pharmaceutical manufacturing system.
- Ierapetritou, M.; Muzzio, F.; Reklaitis, G., Perspectives on the continuous manufacturing of powder-based pharmaceutical processes. AIChE Journal 2016, 62 (6), 1846-1862.
- Wang, Z.; Escotet-Espinoza, M. S.; Ierapetritou, M., Process analysis and optimization of continuous pharmaceutical manufacturing using flowsheet models. Computers & Chemical Engineering 2017, 107, 77-91.
- Boukouvala, F.; Muzzio, F. J.; Ierapetritou, M. G., Dynamic Data-Driven Modeling of Pharmaceutical Processes. Industrial & Engineering Chemistry Research 2011, 50 (11), 6743-6754.
- Rogers, A.; Hashemi, A.; Ierapetritou, M., Modeling of Particulate Processes for the Continuous Manufacture of Solid-Based Pharmaceutical Dosage Forms. Processes 2013, 1 (2), 67-127.
- Boukouvala, F.; Muzzio, F. J.; Ierapetritou, M. G., Predictive modeling of pharmaceutical processes with missing and noisy data. AIChE Journal 2010, 56 (11), 2860-2872.
- Liao, Y.; Deschamps, F.; Loures, E. d. F. R.; Ramos, L. F. P., Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal. International Journal of Production Research 2017, 55 (12), 3609-3629.
- Zhong, R. Y.; Xu, X.; Klotz, E.; Newman, S. T., Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering 2017, 3 (5), 616-630.
- Azarpour, A.; N.G. Borhani, T.; R. Wan Alwi, S.; A. Manan, Z.; I. Abdul Mutalib, M., A generic hybrid model development for process analysis of industrial fixed-bed catalytic reactors. Chemical Engineering Research and Design 2017, 117, 149-167.
- Luo, N.; Du, W.; Ye, Z.; Qian, F., Development of a Hybrid Model for Industrial Ethylene Oxide Reactor. Industrial & Engineering Chemistry Research 2012, 51 (19), 6926-6932.
- Park, S.; Ahn, K. U.; Hwang, S.; Choi, S.; Park, C. S., Machine learning vs. hybrid machine learning model for optimal operation of a chiller. Science and Technology for the Built Environment 2018, 1-12.
- Boukouvala, F.; Niotis, V.; Ramachandran, R.; Muzzio, F. J.; Ierapetritou, M. G., An integrated approach for dynamic flowsheet modeling and sensitivity analysis of a continuous tablet manufacturing process. Computers & Chemical Engineering 2012, 42, 30-47.