Date Approved


Embargo Period


Document Type


Degree Name

Doctor of Philosophy (Ph.D.)


Chemical Engineering


Henry M. Rowan College of Engineering


Kirti Yenkie, Ph.D. & Robert Hesketh, Ph.D.

Committee Member 1

C. Stewart Slater, Ph.D.

Committee Member 2

Mariano Savelski, Ph.D.

Committee Member 3

Wenzhao Wu, Ph.D.


mixed integer linear programming; optimal control; perovskites; pipeline flushing; process optimization; sustainability


Energy production--Environmental aspects


Chemical Engineering | Operations Research, Systems Engineering and Industrial Engineering


According to the World Economic Forum report, the U.S. currently has an energy efficiency of just 30%, thus illustrating the potential scope and need for efficiency enhancement and waste minimization. In the U.S. energy sector, petroleum and solar energy are the two key pillars that have the potential to create research opportunities for transition to a cleaner, greener, and sustainable future. In this research endeavor, the focus is on two pivotal areas: (i) Computer-aided perovskite solar cell synthesis; and (ii) Optimization of flow processes through multiproduct petroleum pipelines. In the area of perovskite synthesis, the emphasis is on the enhancement of structural stability, lower costs, and sustainability. Utilizing modeling and optimization methods for computer-aided molecular design (CAMD), efficient, sustainable, less toxic, and economically viable alternatives to conventional lead-based perovskites are obtained. In the second area of optimization of flow processes through multiproduct petroleum pipelines, an actual industrial-scale operation for packaging multiple lube-oil blends is studied. Through an integrated approach of experimental characterization, process design, procedural improvements, testing protocols, control mechanisms, mathematical modeling, and optimization, the limitations of traditional packaging operations are identified, and innovative operational paradigms and strategies are developed by incorporating methods from process systems engineering and data-driven approaches.