Big Data Analytics of Freeze Drying Process and Equipment

Big Data Analytics of Freeze Drying Process and Equipment

07/17/2020, 10:15 AM - 11:00 AM

Innovation Stage, Booth 1281, Exhibit Hall
Language:
English
Shifting the state-of-the-art from time based scheduling to data-driven decision making is key when a batch of lyophilized product can sometimes cost in excess of 100 million dollars. With 50% of all Biopharmaceuticals now requiring freeze-drying, it is essential that we discuss implementation of big data analytics augmenting pharmaceutical freeze drying in the production environment. The goal of this effort is to develop models that capture equipment behavior through pattern recognition techniques, augmented with real-time sensing to predict failure events before they actually occur and disrupt production. This is implemented using: Step 1 – Historical data analytics Step 2 – Sensor implementation Step 3 – Real-Time system monitoring to predict departures In this presentation, we will summarize a methodology to move towards a predict-and-schedule model for monitoring health of freeze dryer equipment and process, to replace the existing temporal model.

Contributors

  • Arnab Ganguly, PhD

    Speaker

    Technology Manager

    IMA Life

    Arnab received a PhD in the School of Aeronautics & Astronautics at Purdue University in 2014 with a focus on modeling low-pressure water-vapor flows...

  • Vaibhav Kshirsagar

    Speaker

    Product Manager, R&D

    IMA Life North America Inc.

    After completing a B.Tech from the Indian Institute of Technology, Hyderabad in Mechanical Engineering, Vaibhav graduated from Purdue University with...

Objective

  1. Objective 1. Historical data analytics to develop golden batch and equipment characteristics by analyzing 10s of millions of data points from live production data 2. Sensor identification and implementation on production equipment to augment health analytics in real-time production environment 3. Real-Time system monitoring to predict departures from golden batches

Type of Session

  1. Type of Session
    Session

Categories

  1. Track
    Facility Optimization

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