The US DOE’s Carbon Capture Simulation Initiative (CCSI) is focused on the development of state of the art process models to accelerate the development and commercialization of post-combustion carbon capture system technologies. For developing a “gold standard” process model of the MEA-based CO2capture systems, a rigorous thermodynamic framework is developed and integrated with the rigorous properties models developed by the authors. The model is validated with the steady-state data from a pilot plant.
Although deterministic thermodynamic models exist for the MEA-CO2-H2O system, a stochastic model considering uncertainty quantification is also developed in this work. The accuracy of the results from design and optimization studies is contingent upon accurate representation of uncertainty bounds on simulation outputs. For the deterministic model, vapor-liquid equilibrium (VLE), heat capacity, and heat of absorption data from the open literature are used to calibrate the parameters in the e-NRTL thermodynamic framework. A Bayesian methodology is used to develop the stochastic model by considering uncertainty in the model parameters. A surrogate response surface model is developed using Multivariate Adaptive Regression Splines (MARS) to reduce the computational expense of the methodology. Using the experimental data, the Bayesian method provides informed posteriors. The posterior parameter distributions from the thermodynamic model are propagated through the overall process model to determine uncertainty in prediction of outputs of interest (e.g. energy input required for solvent regenerator, CO2capture efficiency).
This model is validated with high quality steady-state data collected over a wide operating range from the National Carbon Capture Center in Wilsonville, Alabama. The key manipulated variables such as solvent flowrate and reboiler steam flowrate and disturbance variables such as the flowrate and CO2 concentration of the inlet flue gas were varied widely in these test runs. Validation studies with the NCCC data help to narrow down the model uncertainty. The study shows that a systematic uncertainty quantification approach using rich experimental data can be very valuable to improve the accuracy of model results.
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