Integration of high-fidelity CO2 sorbent models at the process scale using dynamic discrepancy

A high-fidelity model of a mesoporous silica supported, polyethylenimine (PEI)-impregnated solid sorbent for CO2 capture has been incorporated into a model of a bubbling fluidized bed adsorber using Dynamic Discrepancy Reduced Modeling (DDRM). The sorbent model includes a detailed treatment of transport and amine-CO2-H2O interactions based on quantum chemistry calculations. Using a Bayesian approach, we calibrate the sorbent model to Thermogravimetric (TGA) data. Discrepancy functions are included within the diffusion coefficients for diffusive species within the PEI bulk, enabling a 20-fold reduction in model order. Additional discrepancy functions account for non-ideal behavior in the adsorption of CO2 and H2O. The discrepancy functions are based on a Gaussian process in the Bayesian Smoothing Splines ANOVA framework, which provides a convenient parametric form for calibration and upscaling. The dynamic discrepancy method for scale-bridging produces probabilistic predictions at larger scales, quantifying uncertainty due to model reduction and the extrapolation inherent in model upscaling. The dynamic discrepancy method is demonstrated using TGA data for a PEI-based sorbent and model of a bubbling fluidized bed adsorber. Acknowledgements: This work is supported by the Carbon Capture Simulation Initiative, funded through the Office of Fossil Energy, US Department of Energy

Model Predictive Control of Integrated Gasification Combined Cycle Power Plants

The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques

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

<p>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.</p

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

<p>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.</p

Innovative computational tools and models for the design, optimization and control of carbon capture processes

The development and scale up of cost effective carbon capture processes is of paramount importance to enable the widespread deployment of these technologies to significantly reduce greenhouse gas emissions. The U.S. Department of Energy initiated the Carbon Capture Simulation Initiative (CCSI) in 2011 with the goal of developing a computational toolset that would enable industry to more effectively identify, design, scale up, operate, and optimize promising concepts (Miller et al., 2014). The CCSI Toolset consists of both multi-scale models as well as new computational tools. This paper focuses specifically on the PSE-related computational tools and models that provide new capabilities for integrating multi-scale models with advanced optimization, uncertainty quantification (UQ), and surrogate modeling techniques