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Uncertainty Quantification Through Bayesian Analysis for a Fixed Bed Experiment of Carbon Capture Using Polyethylenimine (PEI) Solid Sorbents Brian Logsdon With greenhouse gas emissions becoming a major concern and topic for research over the past decade, much effort has been supplied into the progress of reducing these emissions. Carbon dioxide concentration has increased over past 60 years. A major source of this emission is post combustion coal power plants. In order to reduce these emissions, many carbon capture and storage technologies are being researched and developed. A major issue confronting this research is investigating these technologies on multiple scales. For example, solid sorbents experience phenomena on a quantum and macroscopic scale. Thus a bridge must be made between these two scales. This thesis investigates a fixed bed experiment, proposes a model for both the flow and adsorption of CO2 & H2O, and then quantifies the uncertainty of parameter estimations made with comparing the model to data. The model and uncertainty quantification was implemented in a C++ tool set. The power of this tool set lies in the ability to extract more information out of bench scale experiments than traditional optimization methods. This leads to better predictions in modeling a larger (process) scale, better understanding of the mathematical model used at the bench scale, and information to design better bench scale experiments to reduce the uncertainty. The results of this analysis with the proposed model showed the posterior predictions covering the real data set. In other words, the posterior distribution includes a set of parameters that are the “true” values. Information on the certainty of each parameter estimation was also obtained in this analysis.

Uncertainty quantification through bayesian analysis for a fixed bed experiment of carbon capture using polyethylenimine (PEI) solid sorbents