David S. Mebane

Carbon Capture Simulation Initiative: A Case Study in Multiscale Modeling and New Challenges

Advanced multiscale modeling and simulation have the potential to dramatically reduce the time and cost to develop new carbon capture technologies. The Carbon Capture Simulation Initiative is a partnership among national laboratories, industry, and universities that is developing, demonstrating, and deploying a suite of such tools, including basic data submodels, steady-state and dynamic process models, process optimization and uncertainty quantification tools, an advanced dynamic process control framework, high-resolution filtered computational-fluid-dynamics (CFD) submodels, validated high-fidelity device-scale CFD models with quantified uncertainty, and a risk-analysis framework. These tools and models enable basic data submodels, including thermodynamics and kinetics, to be used within detailed process models to synthesize and optimize a process. The resulting process informs the development of process control systems and more detailed simulations of potential equipment to improve the design and reduce scale-up risk. Quantification and propagation of uncertainty across scales is an essential part of these tools and models.

Characteristic Thickness for a Dense La0.8Sr0.2MnO3 Electrode

Dense La0.8Sr0.2MnO3 LSM electrodes were patterned by photolithography and fabricated via pulsed-laser deposition on Y2O3-stabalized ZrO2 YSZ electrolytes. Impedance analysis shows that the interfacial polarization resistance decreases significantly as electrode thickness drops below a critical value, beyond which the top surface of the LSM becomes active for oxygen reduction. However, when the LSM electrodes become too thin, the in-plane sheet resistance of the LSM starts to limit the utilization of the electrodes along their length. Quantification of the characteristic thickness is important not only to intelligent design of practical mixed-conducting electrodes but also to electrode design for fundamental studies.

A generalised space-charge theory for extended defects in oxygen-ion conducting electrolytes: from dilute to concentrated solid solutions

The standard Poisson–Boltzmann approach to modeling the near-interface defect behaviour in solid electrolytes performs well at low dopant concentrations but its applicability is questionable at higher dopant levels where interactions become important. Here we present a new approach, which combines Poisson–Boltzmann with the Cahn–Hilliard model for concentrated solid solutions. This ‘Poisson–Cahn’ theory yields activity coefficients for point defects that consider both local and non-local chemical interactions. Taking the fluorite-structured solid solution CeO2–Gd2O3 as a model system, we predict defect behaviour near grain boundaries over the entire concentration range. Examination of the near-interface defect-concentration and electrostatic-potential profiles reveals behaviour that matches existing space-charge theory at low overall dopant concentrations, becoming more complex, in accord with experiment and atomistic simulations, as concentrations increase. Inclusion of an existing conductivity model that considers the effects of local dopant concentration leads to the successful prediction of both bulk and total (bulk + grain boundary) conductivities in both concentrated and dilute cases. The Poisson–Cahn approach is not limited to grain boundaries in oxygen-ion conducting fluorite oxides, but is applicable to all extended defects—grain boundaries, surfaces and dislocations—in ion-conducting systems.

Triple-Phase Boundary and Surface Transport in Mixed Conducting Patterned Electrodes

The mathematical framework required to account for a triple-phase boundary (TPB) and the accompanying surface transport was added to a two-dimensional numerical model of a mixed conducting thin film by considering appropriate kinetic rate and mass-transport expressions. Approximate parameters were chosen so that the model qualitatively matched experimental results for patterned La 1-x Sr x MnO 3±δ (LSM) electrodes, including trends with respect to thickness, active area, cathodic polarization, and sheet resistance. The rate of the TPB reaction was predicted to decrease due to sheet-resistance limitation, although it is expected to be independent of the active area and thickness of the film electrode when the effect of sheet resistance is insignificant. The addition of this feature is vital to the interpretation of patterned electrode experiments and to precise determination of parameters for better prediction of the electrochemical response of patterned LSM electrodes. The implementation and validation of this model is the next step in the development of continuum models useful to a variety of multiscale investigations of SOFC electrodes.

Upscaling Uncertainty with Dynamic Discrepancy for a Multi-Scale Carbon Capture System

ABSTRACT Uncertainties from model parameters and model discrepancy from small-scale models impact the accuracy and reliability of predictions of large-scale systems. Inadequate representation of these uncertainties may result in inaccurate and overconfident predictions during scale-up to larger systems. Hence, multiscale modeling efforts must accurately quantify the effect of the propagation of uncertainties during upscaling. Using a Bayesian approach, we calibrate a small-scale solid sorbent model to thermogravimetric (TGA) data on a functional profile using chemistry-based priors. Crucial to this effort is the representation of model discrepancy, which uses a Bayesian smoothing splines (BSS-ANOVA) framework. Our uncertainty quantification (UQ) approach could be considered intrusive as it includes the discrepancy function within the chemical rate expressions; resulting in a set of stochastic differential equations. Such an approach allows for easily propagating uncertainty by propagating the joint model parameter and discrepancy posterior into the larger-scale system of rate expressions. The broad UQ framework presented here could be applicable to virtually all areas of science where multiscale modeling is used. Supplementary materials for this article are available online.

A Space-Charge Treatment of the Increased Concentration of Reactive Species at the Surface of a Ceria Solid Solution

A space-charge theory applicable to concentrated solid solutions (Poisson-Cahn theory) was applied to describe quantitatively as a function of temperature and oxygen partial pressure published data obtained by in situ X-ray photoelectron spectroscopy (XPS) for the concentration of Ce3+ (the reactive species) at the surface of the oxide catalyst Ce0.8 Sm0.2 O1.9 . In contrast to previous theoretical treatments, these calculations clearly indicate that the surface is positively charged and compensated by an attendant negative space-charge zone. The high space-charge potential that develops at the surface (>0.8 V) is demonstrated to be hardly detectable by XPS measurements because of the short extent of the space-charge layer. This approach emphasizes the need to take into account defect interactions and to allow deviations from local charge neutrality when considering the surfaces of oxide catalysts.

Multi-scale modeling of an amine sorbent fluidized bed adsorber with dynamic discrepancy reduced modeling

Inaccuracy in chemical kinetic models is a problem that affects the reliability of large-scale process models. Detailed and accurate kinetic models could improve this situation, but such models are often too computationally intensive to utilize at process scales. This work presents a method for bridging the kinetic and process scales while incorporating experimental data in a Bayesian framework. “Dynamic Discrepancy Reduced Modeling” (DDRM) enables the use of complex chemical kinetic models at the process scale. DDRM inserts Gaussian process (GP) stochastic functions into the first-principles dynamic model form, enabling a sharp reduction in model order. Uncertainty introduced through the order reduction is quantified through Bayesian calibration to bench-scale experimental data. The use of stochastic functions within the dynamic chemical kinetic model enables this uncertainty to be correctly propagated to the process scale. The model reduction and uncertainty quantification framework was applied to a reaction–diffusion model of mesoporous silica-supported, amine impregnated sorbents; model order reduction of over an order of magnitude was achieved through a sharp reduction in the resolution of the approximate solution for diffusion. GPs of the Bayesian smoothing splines analysis of variance (BSS-ANOVA) variety were applied to the diffusion coefficients and equilibrium constants in the model. The reduced model was calibrated using experimental dynamic thermogravimetric data, with prior probability distributions for physical model parameters derived from quantum chemical calculations. Model and parameter uncertainties represented in the posterior distribution were propagated to a bubbling fluidized-bed adsorber simulation implemented in Aspen Custom Modeler. This work is the first significant demonstration of the dynamic discrepancy technique to produce robust reduced order models for bench-to-process multi-scale modeling and serves as a proof-of-concept.

The Effect of Microstructural Interconnectivity on the Resistivity of Anisotropic Al2O3‐SiCw Composites

Ongoing work aims to relate microstructural properties of Al2O3‐SiCw composites such as whisker orientation and size distributions to the macroscopically measured electrical properties of the composite. The tools of the study include newly developed stereological techniques, impedance spectroscopy and Monte Carlo simulations. The key results will relate the microstructure to electrical properties through theoretically or experimentally determined percolation properties of the composite.