Research

Research Areas

1. Process Systems Engineering
2. Energy and Sustainability
3. Process Intensification
4. Polymer Reaction Engineering
5. Probabilistic Mathematical Modeling, Prediction and Inference
6. Multiscale Modeling

Specific Projects

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Studying Free-Radical Polymerization Reactions through Chemical Quantum Calculations and Spectroscopic Methods

Spontaneous thermal polymerization allows for the production of higher quality, environmentally-friendlier solvent-borne paints and coatings at lower operating costs. We conduct research to better understand the kinetics, the reaction mechanisms, and the relevant intermediates and transition states for initiation and chain transfer reactions in spontaneous thermal polymerization of acrylates using an integrated research strategy, with the ultimate goal of designing "chemically self-regulated" polymerization processes for the production of high-performance acrylic resins. The integrated research strategy includes first-principles density functional theory calculations, design of experiments, batch laboratory experiments, and spectroscopic analyses.

Collaborators: Andrew M. Rappe (UPenn), Michael C. Grady (DuPont)

Funding Agencies: NSF, ACS-PRF, DuPont

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Mathematical Modeling, Analysis, Optimization and Control of Solid Oxide Fuel Cells, Rechargeable Batteries,
and Dye-Sensitized Solar Cells

We develop first-principles mathematical models of the cells and batteries to systematically investigate their steady-state and dynamic behaviors, determine their optimal design specifications and operating conditions, develop monitoring systems, and propose robust and efficient control systems.

Collaborators: Ken Lau

Funding Agencies: NSF

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Dynamic Risk Assessment and Minimization in Processing Plants

We develop systematic methods for synergistic improvement of process safety and product quality using process databases. Our efforts include the introduction and study of new methods for dynamic risk assessment of processing plants and testing the new methods on several industrial plants in collaboration with Air Liquide Research and Development in Newark, DE. Product-quality data are utilized to identify near-misses and prevent accidents more effectively; that is, to achieve improved process safety and product quality in a synergistic way. Among specific research objectives are: (1) efficiently handling large and complex event trees associated with alarm databases, (2) systematically conducting near-miss utilization and management to develop leading indicators, (3) developing a method of identification of special causes from available process information at each time instant, and (4) developing a method of predicting possible near-future accidents from available process information at each time instant. These techniques can be easily utilized in other industries/organizations, such as the aviation, healthcare and nuclear industries.

Collaborators: Warren D. Seider (UPenn), Ulku Oktem (UPenn), Jeffrey Arbogast (Air Liquide), and Olivier Cadet (Air Liquide).

Funding Agencies: NSF