Variability of raw materials and intermediate products is a common problem in many industries, in particular in life sciences, and this remains a factor that must be accommodated in the various unit operations (process steps) to ensure a consistent product quality. In addition to variability of inputs, monitoring a batch process with several phases or varying duration is a key challenge for establishing model predictive control. Combining these challenges with the regulatory constraints, many organizations are experiencing strains on the effectiveness of product development and ultimately it is impacting time to market.
By using a multivariate approach we are able to monitor and model many parameters at the same time. Accordingly, the batch trajectories can be developed from advanced sensors such as spectrometers of different types to fully benefit from the resolution and sensitivity of these. By default the operational time in bioprocesses varies, and often there are different phases. Both in terms of mapping of the operational time to relative time and also for phase transitions this new batch modeling tool has proven effective. The method estimates the batch trajectory and confidence intervals in relative time and;
- Removes dependence of the process time axis
- Allows visualization of how the process evolves independent of sampling rate
- Enables plot of individual variables in relative time
This innovative approach will provide new opportunities to improve quality, and serve to build greater confidence in the market for your products. We will share the most relevant cases where assumption-free modeling of time-dependent process has proven to substantially enhance early fault detection and thus improved process and product quality. Register for this webinar to gain insight in an innovative approach that will help you proactively address challenges in product development, in real-time manufacturing scenarios as well as in the quality control phase of the product.
Dr. Frank Westad,
Chief Scientific Officer
Frank Westad received his Master of Science in Chemistry and Data analysis in 1988, and completed his Ph.D. thesis “Relevance and parsimony in multivariate modeling” in 2000 with the Norwegian University of Science and Technology.
His working experience over the years includes positions as Research Scientist with SINTEF, Consultant with IDT GmbH (Germany), CSO with CAMO and Senior Research Scientist at the Norwegian Food Research Institute (MATFORSK) and GE Healthcare. His experience also includes numerous scientific papers, presentations at international conferences and teaching statistics, chemometrics and multivariate methods for the industry. After being a consultant for CAMO the past two years, he now holds the position of Chief Scientific Officer in the company.
- Learn about a new approach to modeling time dependent processes
- Discover out-of-spec situations at an early stage in process monitoring
- Improve the quality of batch modeling and outputs
- Head of Research and Development
- Quality Control Manager
- Production/Process Engineer
- Operations Manager
- PAT Professionals/Leader
