Modeling, Computation, and Data Analytics in Process Development

Computational and modeling techniques have emerged as critical tools in delivering predictive insights in chemical process development. In-silico tools leverage first principles-based modeling, efficient algorithms, and advanced methodologies in driving efficiency, reproducibility, and robust process performance to aid manufacturing. Such tools are an invaluable resource during the scale-up of processes from lab to commercial production. By simulating and analyzing the effects of scale on process performance, experts can anticipate challenges and enable optimum design of a process. Data analytics, combined with modeling and computation, enables the extraction of valuable information from large datasets, facilitating data-driven decisions, and computer-generated experimental design. This session will highlight talks that leverage modeling and computational strategies to navigate complex challenges to accelerate innovations in process development technologies.