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Process modelling is invariably the basis for the quantitative design and evaluation of chemical processes in general and CO 2 -capture processes in particular. Several points need to be resolved in order to establish the validity and accuracy of the model. How good are the underlying data and models? Where are they weak and where are they strong? Which data are most important to develop reliable models? How accurate should the data be? How large should the design margins be? An effective way to resolve these important points is through uncertainty analysis. Understanding the validity and accuracy of a model through uncertainty analysis can be valuable for determining appropriate plant design margins. This work proposes a practical approach to first ensure that the model properly meets asymptotic limits (e.g., predicts equilibrium behavior for high kinetics or long contact times), and then to devise practical perturbation schemes for calculated properties in order to provide quantitative engineering insight into process uncertainties. Error propagation in process modelling is difficult because simply varying model parameters usually does not give meaningful answers from an engineering perspective, and therefore we have devised a practical scheme to perturb designated calculated properties. The results show that our approach to uncertainty analysis enables the design engineer to gain an intuitive understanding of how the uncertainty of the process models used for solvent-based CO 2 capture affects the process performance, and is complementary to the mathematically complex methods. As an example, specific results obtained in this work are that the most important properties for operations (in particular, solvent flow rate and heat rate) are phase equilibrium and chemical kinetics, followed by packing interfacial area, This paper completes a study that was first presented at the 12th International Conference on Greenhouse Gas Control Technologies (GHGT-12), Austin, TX, October 5–9, 2014. [ABSTRACT FROM AUTHOR]