(512a) Uncertainty Analysis of An Igcc System with Single-Stage Entrained-Flow Gasifier | AIChE

(512a) Uncertainty Analysis of An Igcc System with Single-Stage Entrained-Flow Gasifier


Shastri, Y. - Presenter, Energy Biosciences Institute, University of Illinois at Urbana Champaign
Zitney, S. E. - Presenter, National Energy Technology Laboratory, U.S. Department of Energy

Integrated Gasification Combined Cycle (IGCC) systems using coal gasification is an attractive option for future energy plants. Consequenty, understanding the system operation and optimizing gasifier performance in the presence of uncertain operating conditions is essential to extract the maximum benefits from the system. This work focuses on conducting such a study using an IGCC process simulation and a high-fidelity gasifier simulation coupled with stochastic simulation and multi-objective optimization capabilities.

Coal gasifiers are the necessary basis of IGCC systems, and hence effective modeling and uncertainty analysis of the gasification process constitutes an important element of overall IGCC process design and operation. In this work, an Aspen Plus® steady-state process model of an IGCC system with carbon capture enables us to conduct simulation studies so that the effect of gasification variability on the whole process can be understood. The IGCC plant design consists of an single-stage entrained-flow gasifier, a physical solvent-based acid gas removal process for carbon capture, two model-7FB combustion turbine generators, two heat recovery steam generators, and one steam turbine generator in a multi-shaft 2x2x1 configuration. In the Aspen Plus process simulation, the gasifier is represented as a simplified lumped-parameter, restricted-equilibrium reactor model.

In this work, we also make use of a distributed-parameter FLUENT® computational fluid dynamics (CFD) model to characterize the uncertainty for the entrained-flow gasifier. The CFD-based gasifer model is much more comprehensive, predictive, and hence better suited to understand the effects of uncertainty. The possible uncertain parameters of the gasifier model are identified. This includes input coal composition as well as mass flow rates of coal, slurry water, and oxidant. Using a selected number of random (Monte Carlo) samples for the different parameters, the CFD model is simulated to observe the variations in the output variables (such as syngas composition, gas and ash flow rates etc.). The same samples are then used to conduct simulations using the Aspen Plus IGCC model. The simulation results for the high-fidelity CFD-based gasifier model and the Aspen Plus equilibrium reactor model for selected uncertain parameters are then used to perform the estimation. Defining the ratio of CFD based results to the Aspen Plus result as the uncertainty factor (UF), the work quantifies the extent of uncertainty and then uses uniform* distribution to characterize the uncertainty factor distribution.

The characterization and quantification of uncertainty is then used to conduct stochastic simulation of the IGCC system in Aspen Plus. The CAPE-OPEN compliant stochastic simulation capability allows one to conduct a rigorous analysis and generate the feasible space for the operation of the IGCC system. The stochastic simulation results can later be used to conduct multi-objective optimization of the gasifier using a set of identified decision variables. The CAPE-OPEN compliant multi-objective capability in Aspen Plus can be used to conduct the analysis. Since the analysis is based on the uncertainty modeling studies of the gasifier, the optimization accounts for possible uncertainties in the operation of the system. The results for the optimized IGCC system and the gasifier, obtained from the stochastic simulation results, are expected to be more rigorous and hence closer to those obtained from CFD-based rigorous modeling.