(4ah) High-Fidelity Modeling and Monitoring of Energy Systems

My work has focused largely on the study of energy systems, where increased penetration of renewable energy into the power grid has caused difficulties for more traditional power generation sources as they are forced to adapt their operational strategies to focus on supplementing these intermittent renewable sources of power. These changes can greatly impact equipment health; therefore, monitoring equipment damage is essential to ensure reliable operation of traditional power plants while load-following. However, monitoring equipment health through process data can be difficult, thus modeling these systems is valuable to understanding the impacts of operational changes.

Despite the current popularity of data-driven modeling for its ease of implementation and high accuracy, its capabilities are extremely limited when extrapolating outside of the range of the data used for regression. Models based on first principles are not limited in this way. Further, first-principles modeling allows for unmeasurable variables to be predicted in a physically rigorous way. Therefore, first-principles models can be an important tool in cases where essential measurements are not available. One focus of my work thus far has been to develop a dynamic, distributed-parameter SCPC boiler model based on first principles, which can be used to determine the entire thermal and pressure profile of the boiler. This includes the thermal profiles of the steam tubes and headers, which would be practically impossible to measure in an industrial setting.

I would be interested in continuing my work on the application of first-principles modeling to energy systems in order to study phenomena that are not easily measured in industrial-scale systems and to further the development of monitoring approaches for such processes. I am particularly interested in multi-scale (spatial or temporal) modeling, where there are significant computational difficulties. When combined with available data, first-principles modeling can also be useful for diagnosing the underlying causes of process disruptions, as has been seen with the development of “digital twins”. Exploring ways to leverage first-principles models to advance process monitoring capabilities for industrial processes could also yield valuable contributions.

Additionally, I would be interested in pursuing the utilization of first-principles modeling for optimization. Through the use of models, unmeasurable indicators of damage or process upset can be calculated and optimal operating strategies can be determined to minimize such indicators. This can also be more broadly applied to other chemical systems where the optimization of unmeasurable parameters can be helpful to better achieve operating goals.

Teaching Interests

Out of the core classes in the chemical engineering undergraduate curriculum, I would be particularly interested in teaching the subjects of heat transfer, transport phenomena, numerical methods, reaction kinetics, and reactor design. In addition, I would also be interested in teaching courses focused on process modeling and simulation, including dynamic simulation.