Development of a Rigorous Modeling Framework for Solvent-Based CO₂ Capture. Part 2: Steady-State Validation and Uncertainty Quantification with Pilot Plant Data

Abstract

The U.S. DOE's Carbon Capture Simulation Initiative (CCSI) has a strong focus on the development of state of the art process models for accelerating the development and commercialization of postcombustion carbon capture system technologies. One of CCSI's goals is the development of a process model that will serve not only as a definitive reference for benchmarking of the performance of solvent-based CO 2 capture systems but also as a framework for the development of highly predictive models of advanced solvent systems. In Part 1 of this paper and previous work, submodels for the system were developed, including those for physical properties, kinetics, mass transfer, and column hydraulics, by calibrating model parameters to fit relevant experimental data. For individual submodels, a Bayesian inference methodology was used to refine the estimates of the parameter values and to quantify the parametric uncertainty of the models. This work is focused on incorporating these submodels into a complete process model and validating this model with large scale pilot plant data from the Pilot Solvent Test Unit (PSTU) at the National Carbon Capture Center (NCCC). The model has been validated with data representing a wide range of operating conditions for absorber and stripper columns, including variable packing height and presence of intercooling in the absorber. The uncertainty in the solvent composition is measured by comparing the process measurements at NCCC to standard laboratory techniques of a known uncertainty. Through a sensitivity study, this measurement uncertainty is used to provide insight into some discrepancy between model and data. Parametric uncertainty for various submodel parameters has been propagated through the process model to assess the resulting uncertainty in the key model outputs. Finally, a variance-based sensitivity analysis is used to provide insight into the relative contributions of parameters from various submodels to the overall uncertainty of the process outputs in various operating regimes.

Towards improved guidelines for cost evaluation of carbon capture and storage

“…Like with any probabilistic method however, they are best suited for the variability of input parameters, and less for real uncertainty, given that there may not be a reasonable basis for probability density functions of truly uncertain parameters. [188][189][190] in which surrogate models of an MEA capture plant were built based on a full process model and compared with process data from the National Carbon Capture Center (NCCC) in Alabama, USA [191]. Table 3-5 shows the names of the varied parameters along with sources that contain more details of the sub-model development and uncertainty quantification.…”

“…For the solvent-based CO2 capture system example, the operating space of interest represents feasible combinations of input variables including but not limited to solvent flowrate, flue gas flowrate, CO2 loading in solvent, and CO2 concentration in the flue gas. Some output variables of interest may include CO2 capture efficiency in the absorber and reboiler duty requirement in the stripper [191].…”

Development of Hydraulic Models, Mass Transfer Models, and Dynamic Models of Solvent-Based Carbon Capture Processes with Uncertainty Quantification and Validation with Pilot Plant Data

“…Models for the density, viscosity, and surface tension have been developed as function of temperature and composition using large amount of datasets available in the literature for the MEA-H2O-CO2 system (Morgan et al, 2015). The thermodynamic model has been developed using the e-NRTL thermodynamic framework (Morgan et al, 2017) (Morgan et al, 2018;Morgan et al, 2017;Morgan et al, 2015 ). The model has been found to predict the steady-state data from the NCCC for various key variables, such as CO2 capture efficiency and CO2 loading, satisfactorily over a wide operating range.…”

“…Details of the NCCC pilot-plant have been thoroughly discussed in other publications (Morgan et al, 2017;Morgan et al, 2018). Dynamic experiments were conducted using an absorber configuration of 3 beds with 2 intercoolers.…”

“…It should be noted that these liquid samples capture mass balance closures of CO2 from the liquid side whileFigure 3.6 captures CO2 mass balance from the gas side. Since the gas side measurements have far less uncertainty andFigure 3.6 shows a satisfactory fit to the experimental data, discrepancies between the liquid side measurements and model predictions are attributed to the errors in the experimental measurements, as discussed inMorgan et al (2018) andMorgan (2017). Uncertainties in the liquid side measurements in NCCC were estimated by comparing the measurements with more accurate methods.…”