Research
At the Data-Driven Process Systems Engineering Lab, we work at the intersection of methods development and real-world applications. Our research spans two key areas:
- Methods-Driven Research: We develop novel approaches in data-driven optimization and hybrid modeling, integrating machine learning with fundamental process knowledge.
- Application-Driven Research: We apply our methods to tackle pressing challenges in energy systems and sustainable manufacturing. Our projects blend theoretical advancements with impactful applications to create sustainable and efficient engineering solutions.
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We study different hybridization scenarios for model development, including hybrid modeling and process-informed machine learning. We also use machine learning to expedite phenomenological model discovery. Read our publications on this topic, including a recent invited Perspectives article in Computers and Chemical Engineering.
In this collaborative project, we build models to understand how solids-based processing technology can be used to depolymerize plastics. We start with modeling particle movement within ball mill processes, then develop an integrated flowsheet model and perform technoeconomic, life cycle and supply chain analysis (see publications).