(476b) Uncertainty Quantification in Coal Gasifier Simulations For Reliable Predictive Computational Fluid Dynamics Models | AIChE

(476b) Uncertainty Quantification in Coal Gasifier Simulations For Reliable Predictive Computational Fluid Dynamics Models


Gel, A. - Presenter, Arizona state university
Shahnam, M., National Energy Technology Laboratory

Although global coal consumption rose only by 3.1% (a below average) in 2008, coal remained the fastest growing fuel in the world for the sixth consecutive year.  About 50% of electricity in the United States is being generated by coal.  Demand for electricity is projected to increase by 30% over current demand levels by 2030.  In 2008, global emission of CO2 due to fossil fuel was approximately 30 petagrams, of which the United States accounts for about 19 percent.  Future CO2 emission is likely to be higher, unless considerable changes to the energy system are made.  The U.S. Department of Energy has great interest in technologies that will lead to reducing the CO2 emission of fossil fuel burning power plants.  Pure streams of CO2 can be successfully captured and stored and therefore reduce the impact of CO2 to our atmosphere.  Advanced power generation plants such as Integrated Gasification Combined Cycle (IGCC) can potentially lead to capture and storage of CO2.  In an IGCC power plant, a gasifier is used to convert coal into synthesis gas (syngas).  After removal of impurities and pollutants from the syngas, the resulting gas is used to generate electricity in a gas turbine.

Advanced modeling and simulation capabilities have the promise of significantly reducing the time and cost of the development and deployment of technological processes such as gasification technology. Simulation technology allows rapid scale up of technologies, reducing or even potentially avoiding costly intermediate scale testing. New designs can be tested via simulations to ensure reliable operation under a variety of operating conditions. However, to ensure their usefulness and adoption in practice, the credibility of the simulations needs to be established with uncertainty quantification and probabilistic validation methods. Implementation of uncertainty quantification techniques can also provide guidance for design optimization under uncertainty to achieve robust designs for commercial scale advanced coal gasifiers.

In this study, Computational Fluid Dynamics (CFD), is used to study the quality of syngas, defined as H2/CO and CH4/H2 ratios, in a fluidized bed gasifier under varying operating conditions.  The simulation results will be compared probabilistically to experimental data, which is available for the same fluidized bed gasifier.  In the current study, the effect of variation in critical operating parameters such as coal feed rate, particle size and steam to oxygen ratio on the syngas quality is investigated using a non-intrusive uncertainty quantification techniques including Bayesian analysis and polynomial-chaos expansions. Smolyak sparse grids and optimized Latin hypercube sampling techniques will be used along with adaptive Bayesian optimization to reduce the number of expensive simulations. These sampling techniques enable interrogating correlated random variables with minimum (optimal) samples.

Additionally the extensive experimental data, which is available for the fluidized bed gasifier in this study, allows quantification of model form of uncertainty through Bayesian uncertainty quantification methods.  The resulting discrepancy model (a hybrid combination of simulations and experiments) will be used to verify the validity of the simulation models over the entire design space. The reduced order models will be fully stochastic, i.e., will predict the entire probability distributions of the outputs for every input configuration. The probabilistic nature of these models is inherently suited for modeling risk in addition to the quantities of interest.

These validated predictive models based on CFD models would play a crucial role in scale-up studies as decision support tools and reduce time-to-market deployment of the advanced clean coal energy technologies.