(342m) Development of a Multi-Scale Model with Quantified Uncertainty for a Chemical Looping Process
Chemical looping combustion (CLC) is a novel approach to energy production with an inherent carbon capture capability, relying on metal oxides undergoing reduction-oxidation cycles to transfer oxygen from air to a fossil fuel between a fuel and an air reactor. In the fuel reactor, the fuel reacts with the oxygen carrier, generating an exhaust stream consisting of carbon dioxide (CO2) and water (H2O) from which H2O can be condensed resulting a CO2-rich stream ready for utilization or storage, while in the air reactor, the oxygen carrier is re-oxidized. By avoiding the direct combustion of fuel in air without relying on an expensive air separation unit, CLC is an attractive alternative to conventional CO2 capture technologies. However, CLC processes have not reached commercial realization yet, with widespread research efforts being focused on the development oxygen carriers, as well as on the design of suitable reactors.
Oxidation and reduction reactions in a number of potential oxygen carriers are highly complex and may involve various oxidation states that are difficult, if not impossible, to characterize and quantify as the reactions proceed. In addition, simultaneous diffusion and reaction of several gaseous species inside the evolving core of the oxygen carrier makes it difficult to develop models that can accurately describe the complex mechanisms. In this work, uncertainties in both model form and parameters for the kinetics of an iron-based oxygen carrier are quantified. Using a Bayesian framework, a kinetic model is calibrated to TGA data, quantifying the parameter uncertainty. The model-form uncertainty is quantified by using a Bayesian Smoothing Splines Analysis of Variance (BSS-ANOVA) model. This intrusive UQ enables the uncertainty in the form of joint model parameter and discrepancy posterior to be propagated into the process scale model.
The process-scale model used in this study is a 1-D steady-state, nonisothermal, first-principles model of a counter-current moving-bed (MB) reactor, applied to the simulation of the CLC of methane. Operating in the MB regime has the advantage of attaining high fuel conversion using relatively low solids circulation rates, improving overall process efficiency . The MB model is implemented in the Institute for the Design of Advance Energy Systemsâ (IDAES) equation oriented computational framework, which facilitates the development and solution of large-scale design and optimization problems. The Bayesian UQ framework yields probabilistic predictions of key process variables. Sensitivity to various operating conditions is evaluated providing valuable information on scale-up uncertainty.
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