(342b) Using First-Principles and Data-Driven Models to Guide Inference of Pharmaceutical Reaction Data
- Conference: AIChE Annual Meeting
- Year: 2019
- Proceeding: 2019 AIChE Annual Meeting
- Group: Pharmaceutical Discovery, Development and Manufacturing Forum
Tuesday, November 12, 2019 - 12:55pm-1:20pm
In this presentation, a rigorous and general modeling workflow is described on the application of kinetic models and statistical models to the same set of dynamic reaction data. In particular, a semi-parametric empirical model was chosen. An industrial case study is presented, as showcase of the performance and robustness of the two modeling approaches and their impacts, side by side, on parameter effect estimation, reaction robustness range finding, and reaction optimization and operation window prediction. Emphasis is placed on the impact of data and sampling on the model inference. New and innovative visualization techniques are shared in this article for efficient data and model result interpretation.