Applications of industry 4.0 concepts in continuous pharmaceutical tablet manufacturing process


03rd November 2021 | 10:00am EST | Dr Ravendra Singh, Research Faculty at C-SOPS, Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA |BOOK FREE SEAT FOR THIS WEBINAR

Currently, Industry 4.0 concepts are being applied to the pharma industry to achieve Pharma 4.0 paradigm. Pharma 4.0 reduces the time and resources needed for continuous pharmaceutical manufacturing and also improves the product quality and production consistency. It has many advantages but also have bigger challenges on the applications of artificial intelligence (AI)/machine learning (ML), and advanced control systems because of different level of complexities.

In this presentation I discuss main components of industry 4.0 namely artificial intelligence (AI)/machine learning (ML), modeling, advanced control, and cloud based data management system for the continuous pharmaceutical manufacturing process.

Four machine learning (ML) models have been trained to predict the response of continuous pharmaceutical manufacturing process and the performance of these ML models has been compared. The investigated ML methods are long short term memory (LSTM), 1D convolution neural network (CNN), random forest (RF), and artificial neural network (ANN). The best performing ML model is then implemented into the continuous pharmaceutical tablet manufacturing process. An advanced model predictive control (MPC) system coupled with an RTD based control system has been also implemented in the continuous pharmaceutical manufacturing (CPM) pilot-plant [1]. The CPP’s and CQA’s are controlled in real time using advanced model predictive control (MPC) system while the none-confirming products are diverted in real time in waste using RTD based control system to assure the final CQA’s of qualified tablet lots. This bi-layer coupled control strategy assures the final product quality, improves production efficiency, minimizes the need of offline testing, and facilitated the real time release. All the relevant data generated during continuous manufacturing has been systematically collected, stored and organized in a data hub (OSI PI) and cloud system as per industry 4.0 standard.

The objective of this presentation is two-fold; first to highlight the pharma 4.0 technology and then demonstrate the development and implementation of machine learning (ML) and advanced control systems into continuous pharmaceutical tablet manufacturing process.

 

Presented by Dr Ravendra Singh, Research Faculty at C-SOPS, Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA

Dr. Ravendra Singh is research faculty at C-SOPS, Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA. He is the recipient of the prestigious EFCE Excellence Award from the European Federation of Chemical Engineering. His research focus is systems engineering including process modeling, digital twin, process control, process optimization, artificial intelligence/machine learning, QbD, PAT, cyber-physical security, novel methodology and software development. His current application domains are the continuous manufacturing of pharmaceutical tablets, drug substances (API), and biopharmaceuticals. He is PI/Co-PI of several projects funded by FDA, NSF, and other companies. Currently, he is leading an Industry 4.0 project sponsored by FDA. He has published more than 70 papers, edited one pharmaceutical systems engineering book published by Elsevier, written more than 12 book chapters, and presented at over 115 conferences. He is actively serving as a Journal editorial board member and conference session chair.


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