With increasing pressure of reducing carbon dioxide to mitigate climate change it is becoming essential to quickly implement post-combustion capture technologies and even reduce the amount of emissions that are being produced. The technology to reduce and capture emissions has been around for several decades now but has rarely been utilized at scale due to high costs associated with capturing CO
2. Intensification of these processes can aid in the implementation of the technology by pairing advanced and hybrid technologies into already well established, standard operations, three cases of which will be shown in this work. The first is modeling and placement optimization of intensified packing in solvent absorption towers. Heat generation from the absorption of CO
2 reduces efficiency of mass transfer by reducing the driving force for mass transfer. Intensified packing has built in channels for a cooling medium to remove heat from the column in-line which an intercooler of incapable of doing, but optimization of the placement of the packing needs to be considered to balance thermodynamic and physical performance. Second intensification case looks at implementing layered solid sorbents in a rotating packed bed (RPB). Different sorbents used for carbon capture come with different advantages and disadvantages making one sorbent better in certain scenarios depending on conditions such as temperature and partial pressure of CO
2. The RPB system is a solid bed which rotates around a central axis (and gas flowing along the axial direction) with separate sections of the bed for simultaneous loading and unloading of the bed. Unlike fixed and moving beds, the RPB can benefit from implementing layers or blends of sorbents that can enhance capture performance by optimally placing sorbents in the best location in the bed. The final intensification work involves scheduling optimization of a hybrid NGCC power plant and hydrogen fuel production with a carbon capture system. Using forecasted LMP data, power plant load can be optimized to determine the best electricity price trends that are best for capturing CO
2 and generating and storing hydrogen that can be cycled back to the power plant at high price periods for clean power generation.
Research Interests
My work focuses on developing efficient, scalable solutions for emissions reduction and clean energy systems through advanced modeling, simulation, and optimization. I have experience with absorption processes, process intensification, and hybrid system design, using tools like IDAES and Pyomo to build dynamic and reduced-order models. My goal is to support practical, data-driven decision-making for process improvement and cost reduction. I enjoy working in interdisciplinary settings and collaborating across technical domains to solve complex engineering challenges.
