Abstract :
Supplying the growing demand for energy and chemicals while at the same time reducing greenhouse gas emissions will be one of the grand challenges that we humans must face in this century. Two promising approaches that can drastically reduce greenhouse gas emissions are to (1) produce electricity using renewable energy such as solar and wind and (2) reduce energy consumption by making the production processes more efficient. Thanks to technological advances and policy incentives, the costs of wind and solar power have significantly reduced in the past two decades. However, both these energy resources are intermittent in nature and batteries are too expensive to be deployed at grid-scale. Another promising but not too well known approach is to use integrated thermal energy storage (TES) in concentrated solar power (CSP) plants. It is estimated that CSP plants integrated with TES can contribute to 11% of the global electricity generation by 2050. The commercially deployed CSP plants, which are typically integrated with two-tank molten-salt sensible heat storage unit, however, are less efficient because they deliver heat to the power block at low temperature. Furthermore, the energy density of molten-salt systems is low and it can become too expensive to be used in large-scale systems. Thermochemical energy storage (TCES), which is based on converting solar-thermal energy to chemical energy, is a promising option because it allows high operating temperatures, high storage density, and low heat loss over long periods.Today, TCES is in its infancy due to, among other reasons, the system complexity. It remains unclear which configuration and reaction should be used for the integrated CSP-TCES system. To this end, in the first part of the talk, I will describe our optimization model for the design and operation of CSP plants under seasonal solar variability. I will also present our analysis that allows us to develop general guidelines for the selection of optimal reaction and process configuration. Process intensification (PI) aims to make processes more efficient by utilizing the synergy between multifunctional phenomena at different time and spatial scales. The phenomena are accurately described by nonlinear algebraic partial differential equation (NAPDE)-based model. It is known that if a process is not optimally designed and operated, its performance can be significantly impacted. However, due to the system complexity and computationally expensive nature of the model, it is challenging to obtain optimal design and operating conditions. In the second part of the presentation, I will describe an optimization algorithm especially designed to handle such models and demonstrate the results on standard test libraries and an integrated carbon capture and conversion process.
Bio sketch :
Ishan Bajaj received his B.Tech. in Chemical Engineering from Indian Institute of Technology, Bombay. He received his PhD in Chemical Engineering from Texas A&M University in 2019 for his work on derivative-free optimization algorithms. Then, he joined the research group of Prof. Christos Maravelias as a postdoctoral research associate in the Department of Chemical and Biological Engineering at University of Wisconsin-Madison, where he worked on multi-scale optimization of integrated thermochemical energy storage in solar power systems. He also worked on modeling and analyses of biomass conversion processes in collaboration with the research groups of Profs. Dumesic and Huber. He is currently a postdoc at Andlinger Center for Energy and the Environment at Princeton University, where he is investigating the container packing problem and simultaneous optimization of material properties, design, and operations of CSP-TCES systems. Due to his contributions in process systems engineering and other professional achievements, he has received several awards including PSE young researcher and FOCAPO travel award from CAST division of AIChE and distinguished graduate research award from Chemical Engineering Department at Texas A&M University.