Applications of industry 4.0 concepts in continuous pharmaceutical tablet...

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Presented by Dr Ravendra Singh, Research Faculty at C-SOPS at Rutgers University, NJ, USA

About this talk

Full Title: Applications of industry 4.0 concepts in continuous pharmaceutical tablet manufacturing process Currently, Industry 4.0 concepts are being applied to 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. 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. 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.

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