Marianthi Ierapetritou, Bob and Jane Gore Centennial Chair of Chemical & Biomolecular Engineering University of Delaware

"Navigating the New Digital Landscape using Process Systems Engineering Tools"

Abstract:

The manufacturing industry is rapidly embracing tools and methodologies enabled by the digital transformation. The surge in data, coupled with advancements in machine learning and artificial intelligence, serves as catalysts for this revolutionary shift.

Process Systems Engineering (PSE) community needs to adapt the methodologies to fully leverage the additional resources for accurate representation of processes and detailed analyses. The approaches can facilitate process development, system analysis, and optimization, supporting goals in areas like sustainability, circular economy, and public health.

Process modeling continues to be an essential tool in PSE, serving to describe complex physical processes and their interconnections. Integrating information across diverse scales poses challenges, as does determining the level of data inclusion. To acquire comprehensive process understanding, efficient tools such as sensitivity analysis, feasibility analysis, life cycle assessment (LCA), and technoeconomic analysis (TEA) can be applied for analysis with data from experiments, pilot plants, databases, and/or first-principle models.

Using the developed models, optimization can be performed to identify optimal conditions of the most important variables identified using sensitivity analysis, while satisfying important operability and product quality constraints. Such in silico optimization results can provide insights to the experimental work, but as model complexity increases, the optimization task becomes computationally demanding. When implementing these tools, addressing data uncertainty emerges as a crucial concern. Employing uncertainty quantification (UQ) approaches becomes essential to tackle this issue.

In this talk, we will discuss our group’s work towards developing these tools and highlight their application in pharmaceutical advanced manufacturing and towards sustainable chemical production.