In this work, a model of an entrained bed gasifier has been developed for improved synthesis gas production. The gasification model considers five stages: pyrolysis, volatiles combustion, char combustion, gasification and a final gas equilibrium zone. Energy integration within the reactor section has been carried out by accounting all the energy consumed and generated by the process. Furthermore, this model is envisaged to evaluate energy losses as well as external heat supplies or refrigeration requirements in order to keep a steady reaction operation.
Subsequent raw gas cleaning operations have also been modelled. They comprise fly ashes, ammonia, sulphur, and metals removal. Other indispensable operations are also included to minimize the environmental effects of the effluents of the gasification plant: a waste water stripping unit and a modified Claus plant for sulphur recovery.
Aspen Hysys (AH) is chosen as the platform for the overall process simulation of the entrained bed gasifier because of its capability for developing custom unit operation models as extensions, which become part of the simulation as any other AH object. Available AH models have been used for conventional process operations, while custom models have been developed for more specific stages within the gasification process, as pyrolysis, char combustion and gasification reactions; also in gas purification units, as the COS hydrolysis reactor and the Claus plant.
Thorough simulations were necessary to help finding out feasible operating conditions of the process to achieve better process performance. The solution adopted is able to predict the composition of the produced gas under various operating conditions (temperature, fuel/carburant ratio, etc.) and from different fuels mixtures (coal, wastes mainly, and biomass in limited proportions).
ELCOGAS data has been used in the platform testing and validation studies. The proposed gasification plant model improves the understanding of the whole process, alternative process synthesis assessment and can be also used as an accurate predictive tool at the optimization stage towards an optimum design.
Acknowledgements:
Financial support received from the European Community projects (RFC-CR-04006), the Generalitat de Catalunya with the European Social Fund and the Ministerio de Educación y Ciencia is fully appreciated. ELCOGAS provision of data is also acknowledged with thanks.