2023 AIChE Annual Meeting

Session: Data-driven approaches, ML/AI and model uncertainty to better process understanding

Chair

Co-Chairs

Ferguson, S., University College Dublin

Presentations

03:30 PM

(628a) Application of Experimental Design Via Bayesian Optimization (EDBO) to Pharmaceutical Process Characterization and Control Strategy Design
Tabora, J., Williams, M., Stevens, J., Fu, J., Li, J., Reyes-Luyanda, D., Skliar, D.

03:55 PM

(628b) Leveraging Small Data for Enhanced Process Development
Blanco, D.

04:20 PM

(628c) Development of Experimentally Validated Machine Learning (ML) Based Model to Predict the Thawing Time of Biologics during Large Scale Freeze-Thawing Cycles
Nagarajan, V., Chaudhuri, B., Duran, T., Mehta, T., Luo, Y., Wang, Y., Minatovicz, B., Fontana, L.

04:45 PM

(628d) Predictive Modeling for Glycan Method Analysis and Species Prediction
Nisal, A., Gabeau, F., Miller, K.

05:10 PM

(628e) Optimal Reaction Pathway Design Using Computer-Aided Process Chemistry
Lee, S., Jensen, K. F.

05:35 PM

(628g) Verification, Validation, and Uncertainty Quantification for Computational Modeling and Simulation in the Pharmaceutical Industry
Kenyon, R., Sirasitthichoke, C., Horner, M., Molaei Chalchooghi, M., Knapp, B.