Dr. Rahul Sangodkar is currently a Senior Engineer at Amgen in Cambridge, MA. Dr. Sangodkar earned his Ph.D. in Chemical Engineering from University of California, Santa Barbara and Bachelor of Chemical Engineering from the Institute of Chemical Engineering. While at University of California, Santa Barbara, Dr. Sangodkar's research emphasized controlling hydration and crystallization of heterogeneous inorganic materials, including biomineralized carbonates, aluminosilicate cementitious solids, and nanoparticle semiconductors.
Reaction Engineering for Robust & Scalable Process Design
Regression-based and predictive models for chemical reaction kinetics are critical to small-molecule commercial process development, process characterization, tech transfer, and continuous manufacturing activities. The development and utilization of such robust models minimize the need for labor-intensive empirical experiments and facilitate an improved conceptual understanding of the reaction chemistry. Importantly, reaction engineering models enable quick in silico scenario assessments to establish design space(s) and/or a quantified rationale for equipment (platform) selection, especially for continuous manufacturing. Such models have been developed, validated, and regularly employed at Amgen to support design and at-scale demos for diverse synthetic reactions, including amide-bond formation, epoxidation and epimerization.
Despite their importance, reaction engineering models have often had limited capabilities to integrate with more complex modeling methods (e.g., computational fluid dynamics), can often require time consuming manual data transfers, and the final models are exceedingly challenging to deploy to non-modeling-SMEs who are integral to the project teams. To mitigate these challenges, the COMSOL modeling platform was evaluated to understand platform opportunities for developing, deploying and updating models for reaction kinetics. Notably, the COMSOL models were demonstrated to be seamlessly deployable to non-modeling-SMEs (with no modeling experience) via ‘web apps’, which correspondingly facilitates accelerated utilization of the models and empowers on-demand predictions. In combination, the reaction engineering capabilities and new modeling platform are expected to provide opportunities for improved engagement for model utilization and increased efficiencies towards delivering robust and well-characterized processes.
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