Diamine-appended metal-organic frameworks exhibiting step-shaped CO2 adsorption are exceptional candidates for energy-efficient carbon capture. However, there are few studies examining their performance in real-world capture scenarios, in part due to the challenge inherent in modeling their CO2 uptake behavior. Here, we develop a dual-site Sips model to fit experimental CO2 adsorption data for dmpn-Mg2(dobpdc) (dmpn = 2,2-dimethyl-1,3-diaminopropane; dobpdc 4-= 4,4′-dioxidobiphenyl-3,3′-dicarboxylate) and develop a linear driving force model for the adsorption kinetics based on available experimental data. These models are used to develop a dynamic, fixed bed, non-isothermal contactor model using shaped particles of the material, which is validated with experimental breakthrough data. We also examine the effects of the high heat of adsorption of the material on CO2 uptake performance and find that heat removal is essential to maximize capture performance. We finally investigate "basic" (no bed cooling during adsorption) and "modified" (bed cooling during adsorption) temperature swing adsorption (TSA) processes using dmpn-Mg2(dobpdc) and their process economics are compared to a state-of-the-art monoethanolamine (MEA) capture system, with and without heat recovery. In the absence of heat recovery, the adsorbent systems are more costly than established technology. However, with 85% heat recovery, both adsorbent-based TSA processes are projected to cost less than the MEA system. This work highlights that thermal management is vital for implementation of dmpn-Mg2(dobpdc) as a viable CO2 capture technology. Investigation of other contactor technologies that can provide unique ways to manage system heat represent promising future areas of study.

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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.

Traditional energy production plants are increasingly forced to cycle their load and operate under low-load conditions in response to growth in intermittent renewable generation. A plant-wide dynamic model of a supercritical pulverized coal (SCPC) power plant has been developed in the Aspen Plus Dynamics® (APD) software environment and the impact of advanced control strategies on the transient responses of the key variables to load-following operation and disturbances can be studied. Models of various key unit operations, such as the steam turbine, are developed in Aspen Custom Modeler® (ACM) and integrated in the APD environment. A coordinated control system (CCS) is developed above the regulatory control layer. Three control configurations are evaluated for the control of the main steam; the reheat steam temperature is also controlled. For studying servo control performance of the CCS, the load is decreased from 100% to 40% at a ramp rate of 3% load per min. The impact of a disturbance due to a change in the coal feed composition is also studied. The CCS is found to yield satisfactory performance for both servo control and disturbance rejection.

Rigorous process models are critical for reducing the risk and uncertainty of scaling up a new technology. It is essential to quantify uncertainty in key submodels so that uncertainty in the overall model can be appropriately characterized. In solvent-based postcombustion CO2 capture technologies, mass transfer and column hydraulics are key factors affecting the performance of the absorber. Developing submodels for mass transfer, column hydraulics, and reactions is a challenging multiscale problem since the phenomena are tightly coupled and it is difficult to design experiments to isolate each properly. In particular, simultaneous mass transfer coupled with fast reaction kinetics makes it difficult to measure the mass transfer rate and reactions rate individually. The typical approach to solving this issue is to use proxy systems to conduct experiments under mass-transfer-limited or reaction-limited conditions. This approach can lead to inaccurate mass transfer submodels. In this paper, a novel simultaneous regression approach is proposed where submodels for mass transfer, diffusivity, interfacial area, and reaction kinetics are optimally identified using experimental data from multiple scales and operating conditions. Since all models have some level of uncertainty, a rigorous uncertainty quantification (UQ) technique is implemented for the hydraulic and mass transfer submodels based on Bayesian inference. Posterior distributions of submodel parameters are propagated through the column model to obtain the uncertainty bounds on critical performance measures.