(259g) Development of Data-Driven and Hybrid Models for Continuous Pharmaceutical Manufacturing Lines Under Industry 4.0 Framework

Chen, Y., Rutgers, The State University of New Jersey
Ierapetritou, M., Rutgers, The State University of New Jersey
Continuous manufacturing has been a critical research area in the pharmaceutical industry for the past decade1. With intensive collaboration among regulatory agencies, leading industrial players and academia, a large number of data-driven and first principle models have been developed to guide the design and simulation process2. Data-driven models attempt to capture the system behavior using only input and output data from the manufacturing line3. Even though they are accurate for specific datasets, the models are highly dependent on the choice of training data, and their description of process physical phenomena is not explicit4. First principle based models simulate the process with known mechanistic equations, but it is hard to model the complex behavior of the line completely5. Recent development in Industry 4.0 has led to a focus in cyber-physical system, internet of things, and smart manufacturing6, 7, and combined with the quality by design initiative from the FDA, the volume of data generated for the entire manufacturing cycle increases dramatically, allowing for a better application of the generated data. As a data science tool, machine learning can be used for analyzing the large data set, and the resulting data-driven models can be used to enhance first principle models for the development of a hybrid model. This similar approach has been done in other areas like catalytic reactor modeling8, 9 and HVAC system analysis10.

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.

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