Energy use around the world will rise in the coming decades. Renewable energy sources will help meet this demand, but renewable sources suffer from intermittency, uncontrollable power supply, geographic limitations, and other issues. Many of these issues can be mitigated by introducing energy storage technologies. These technologies facilitate load following and can effectively time-shift power. This analysis compares dedicated and synergistic energy storage technologies using energy efficiency as the primary metric.
Energy storage will help renewable sources come to the grid, but fossil fuels still dominate energy sources for decades to come in nearly all projections. Carbon capture technologies can significantly reduce the negative environmental impact of these power plants. There are many carbon capture technologies under development. This analysis considers both the innovative and relatively new cryogenic carbon capture™ (CCC) process and more traditional solvent-based systems. The CCC process requires less energy than other leading technologies while simultaneously providing a means of energy storage for the power plant. This analysis shows CCC is effective as a means to capture CO2 from coal-fired power plants, natural-gas-fired power plants, and syngas production plants.
Statistical analysis includes two carbon capture technologies and illustrates how uncertainty quantification (UQ) provides error bars for simulations. UQ provides information on data gaps, uncertainties for property models, and distributions for model predictions. In addition, UQ results provide a discrepancy function that can be introduced into the model to provide a better fit to data and better accuracy overall.



College and Department

Ira A. Fulton College of Engineering and Technology; Chemical Engineering



Date Submitted


Document Type




First Advisor

Larry L. Baxter

Second Advisor

Randy S. Lewis

Third Advisor

Dean R. Wheeler

Fourth Advisor

Matthew J. Memmott

Fifth Advisor

Joel D. Kress


cryogenic, carbon capture, uncertainty quantification, energy storage, syngas