The production of high-purity hydrogen via steam gasification of coal has been investigated. The separation of CO from H 2 in the gasification products is achieved by CO oxidation to CO 2 followed by uptake of the CO 2 by a suitable removal agent. This uptake of CO 2 increases the extent of the water gas shift reaction and enhances the yield and purity of H 2 . In addition to the water gas shift reaction, the oxidation is enhanced by the use of a solid oxygen transfer agent (Fe 2 O 3 ) in the hydrogen enrichment pass. Subsequently, the reduced oxygen transfer agent is reoxidized (and thus regenerated) in the presence of air and the heat liberated via the exothermic reaction is utilized to regenerate carbon dioxide removal agent. In this study, the effect of process variables on coal gasification and hydrogen enrichment has been evaluated. Fixed bed gasification studies using coal and coal-Fe 2 O 3 and coal-CaO mixtures were conducted to evaluate the kinetics of gasification and separation effectiveness of the process. Finally, a bench scale fluidized bed reactor was employed to study the efficacy of the simultaneous gasification-hydrogen enrichment process. The reactions were conducted in the temperature range of 670-900 °C at atmospheric pressures. The results from the fundamental studies, the fixed bed reactor studies, and the fluidized bed reactor studies are presented.

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-The kinetics of Fe 2 O 3 →FeO reaction was investigated. The thermogravimetric (TGA) data covered the reduction of hematite both by pure species (nitrogen diluted CO or H 2 ) and by their mixture. The conventional analysis has indicated that initially the reduction of hematite is a complex, surface controlled process, however once a thin layer of lower oxidation state iron oxides (magnetite, wüstite) is formed on the surface, it changes to diffusion control. Artificial Neural Network (ANN) has proved to be a convenient tool for modeling of this complex, heterogeneous reaction runs within the both (kinetic and diffusion) regions, correctly considering influence of temperature and gas composition effects and their complex interactions. ANN's model shows the capability to mimic some extreme (minimum) of the reaction rate within the determined temperature window, while the Arrhenius dependency is of limited use.