Uncertainty Analysis of Computational Models and Tools for CO2 Capture Processes

Abstract

Advanced capabilities of the modeling and simulation tools has been a major contributor in the research and development of the solvent-based post combustion CO2 capture process. Physical and chemical properties characterizing the solvent performance are fundamentally essential to accurately evaluate the process design and cost of solvent-based CO2 capture process. However, they present inherent uncertainties. To account for the inevitable uncertainties in the solvent property models, the aim of this thesis is to integrate a systematic uncertainty quantification (UQ) framework with the solvent-based CO2 capture process models. Although, recent published literature shows that UQ research area has been employed for solvent-based CO2 capture technology, a comprehensive assessment of various UQ methodologies is currently lacking within this field. Therefore, with the use of established UQ software tools, this thesis explores the implementation of various traditional and more advanced UQ methodologies to develop a better understanding of the kind of information that can be generated and the implications of the results on the expected operating envelope of the CO2 capture process design.

The integration of UQ with process models can provide valuable understanding of the impacts of uncertainty related to solvent properties especially whenusing novel solvents. While ongoing research is being conducted to find solvents with enhanced performance, larger uncertainty in the solvent properties and model suitability will be expected for these novel solvents, due to the scarcity of detailed experimental data. A common way to alleviate these uncertainties is to pass through in-depth testing via pilot and demonstration-scale activities. However, these are not only costly, but they also require significant time and resources. Better understanding of the sources and impacts of uncertainties in the computational tools can enable development of higher accuracy model predictions, leading to significant saving in cost and time.