Improving uncertainty evaluation of process models by using pedigree analysis. A case study on CO₂ capture with monoethanolamine

Abstract

This article aims to improve uncertainty evaluation of process models by combining a quantitative uncertainty evaluation method (data validation) with a qualitative uncertainty evaluation method (pedigree analysis). The approach is tested on a case study of monoethanolamine based postcombustion CO₂ capture from a coal power plant. Data validation was used to quantitatively assess the uncertainty of the inputs and outputs of the MEA model. Pedigree analysis was used to qualitatively assess the uncertainty in the current knowledge base on MEA carbon capture systems, the uncertainty in the MEA process model, and the uncertainty of the MEA model results. The pedigree review was done by 13 international experts in the field of postcombustion carbon capture with chemical solvents. The data validation showed that our MEA model is accurate in predicting specific reboiler duty, and CO₂ stream purity (4% and 1% difference respectively between model and pilot plant results), but in first instance it was less accurate in predicting liquid over gas ratio, and cooling water requirement (54% and 23% difference respectively between model and pilot plant results). The pedigree analysis complemented these results by showing that there was fairly high uncertainty in the thermodynamic, and chemistry submodels, as reflected in the low pedigree scores on most indicators. Therefore, the model was improved to better resemble pilot plant results. The results indicate that using a pedigree approach improved uncertainty evaluation in three ways. First, by highlighting sources of uncertainty that quantitative uncertainty analysis does not take into account, such as uncertainty in the knowledge base regarding a specific phenomenon. Second, by providing a systematic approach to uncertainty evaluation, thereby increasing the awareness of modeller and model user. And finally, by presenting the outcomes in easy to understand numerical scores and colours, improving the communication of model uncertainty. In combination with quantitative validation efforts, the pedigree approach can provide a strong method to gain deep insight into the strengths and weaknesses of a process model, and to communicate this to policy and decision-makers.