Machine Learning for Pharmaceutical Process Control and Monitoring:

Revolution or Evolution?


3-6 March 2019, North Bethesda, Maryland, USA

John Mack - Perceptive Engineering

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Process Analytical Technology is one of the most exciting and important advanced technology areas meeting both the current challenges, as well as the future needs of industry. Each year the IFPAC Scientific Board consisting of industry, regulatory, and academic leaders works collaboratively to create a comprehensive program that addresses important mainstay topics, as well as the latest trends, technologies and applications.

IFPAC brings together experienced professionals and the next generation of leaders to share in discussions, give input and get ideas for the future well-being of the industry. Join your colleagues... the people who are the foundation of Process Analytical Technology (PAT), QbD, Process Understanding & Control, and Real-Time Analytics.


ABSTRACT

Four terms that have achieved a high level of both media and industry interest over the past three years are Industry 4.0, Internet of Things (IOT), Artificial Intelligence (AI) and Machine Learning (ML). The algorithms underpinning ML are designed to overcome following strictly static program instructions by making data-driven predictions or decisions [1]. The power of Machine Learning in particular, is often described as the ability to leverage value from data. For the Pharmaceutical industry, statistical and mechanistic models are already routinely used to obtain value from data, by predicting process and product behaviour in real-time.

Advanced Process Control (APC) and Multi-Variate Analysis (MVA) are well established tools within the process industries. These technologies use data driven techniques such as regression and classification to maximise product quality whilst improving process robustness and agility. When we compare these “traditional” data-driven techniques with Machine Learning we see the same algorithms being applied and a common goal of improved decision making through data analysis and observation being aspired to.

In this talk, case studies from the Pharmaceutical and other industries are used to demonstrate the application of several forms of supervised learning for process control and optimisation. The case studies present applications for the rapid development of synthesis, granulation and crystallisation processes.

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