(368ag) Systems Engineering, Multi-Scale Mechanistic Modeling, and Control for Development Continuous Manufacturing Processes | AIChE

(368ag) Systems Engineering, Multi-Scale Mechanistic Modeling, and Control for Development Continuous Manufacturing Processes

Authors 

Dighe, A. - Presenter, Massachusetts Institute of Technology
Research Interests:

Process Development, Systems Engineering, Multi-scale Mechanistic Modeling, Model-predictive Control, Continuous Manufacturing

Poster abstract:

Continuous manufacturing methods have the potential to resist supply chain uncertainties while maintaining or even improving product quality and quantity. However, the process development of continuous manufacturing methods is not trivial. The underlying kinetic parameters that govern the output of continuous processes are sensitive to changes in operating conditions, which, in turn, prevents the development of efficient control strategies and results in a complex workflow. To overcome such process uncertainties and develop robust continuous manufacturing methods, a mechanistic understanding of the underlying kinetics and the uncertainties in the model parameters is necessary. This task requires coupling various engineering concepts such as systems engineering, multi-scale mechanistic modeling, and model-predictive control.

During my PhD and postdoctoral research, I have had the opportunity to develop expertise in all of the engineering concepts critical for the efficient development of continuous manufacturing processes. My current postdoctoral work supports the development of continuous chromatographic purification of mRNA. In this work, I have developed a mechanistic model and model-predictive control for the column-free continuous chromatography method. During my PhD, I had the opportunity to use multi-scale modeling methods to understand the nucleation and growth of organic crystals and metal-organic frameworks. The PhD research also supported the development of a high-throughput continuous crystallization platform.

I aim to use my expertise in experimental and computational research to gain an in-depth understanding of process kinetics and its relation to process output and help industries transition into more efficient continuous manufacturing methods.