April 10-14, 2016
Current concerns about atmospheric carbon levels have sparked demands for massively reduced carbon emissions. These demands, both environmental and regulatory, exceed the capacity for near-term deployment of emission free technologies. Therefore, to simultaneously meet carbon emission targets and supply the vast global energy demands in the foreseeable future, energy generation must incorporate carbon-capture and sequestration technologies on point source CO2 emitters such as coal-fired power plants.
Coal-fired power plants have long been the primary energy pillar of industrialized nations, and while reduced utilization is an important target for CO2 reduction, coal combustion remains the most intensive and prevalent point emission source, and represents hundreds of billions of dollars globally in infrastructure that cannot easily be abandoned. As such, it is the most important target for carbon-capture technologies, particularly retrofit technologies that could allow for near-term, relatively rapid deployment in current infrastructure.
The present work supports near-term deployment of oxy-coal combustion as a CO2 capture strategy through computer modeling for both retrofitted boilers and new construction. Oxy-coal combustion is a clean coal technology that uses either a high purity O2 stream, or a mixture of O2 and recycled flue gas to burn coal and produce a CO2 emission stream for capture without the need for post-combustion separation. This combustion environment is a radical departure from the air-fired pulverized coal boiler. Exa-scale Computational Fluid Dynamics (CFD) modeling codes enable relatively low-cost, rapid design in this new environment; however, they depend on physically accurate and predictive sub-models. The present work improves the predictive capacity and quantifies the uncertainty of the Carbon Conversion Kinetics oxy-fuel (CCK/oxy) code, a comprehensive coal char conversion model designed to predict coal char burnout in the intense oxy-fuel environment. A recent sensitivity analysis of the CCK/oxy model strongly implicated the char thermal deactivation routine as a key sub-model to accurately predict char conversion, and the present work uses Bayesian uncertainty quantification and calibration in conjunction with literature data to quantify and reduce the uncertainty in the vital annealing sub-model.
The suite of Bayesian statistical tools used here were originally extended to facilitate computational design of CCS technologies. Specifically, the tools used here are based upon GPMSA (Gaussian Process Models for Simulation Analysis), and provide the capability for relatively low-cost calibration, uncertainty quantification, sensitivity analysis, and model response prediction based upon a reasonable number of model executions. We present the results of the calibration using these tools which incorporate the information from both empirical physical measurements and detailed physics-based simulation models.
Acknowledgements: The work at LANL is supported by the Carbon Capture Simulation Initiative with funding through the Office of Fossil Energy, US Department of Energy. The work at BYU is supported by the Department of Energy, National Nuclear Security Administration, under Award Number DE-NA0002375.
Troy Holland, Sham Bhat, Peter Marcy, James Gattiker, Joel Kress, and Thomas Fletcher, "Baysian uncertainty quantification and calibration of a clean-coal design code" in "CO2 Summit II: Technologies and Opportunities", Holly Krutka, Tri-State Generation & Transmission Association Inc. Frank Zhu, UOP/Honeywell Eds, ECI Symposium Series, (2016). http://dc.engconfintl.org/co2_summit2/25