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Balaji Sridharan

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Journal article (2020) - Mara Del Grosso, Balaji Sridharan, Christos Tsekos, Sikke Klein, Wiebren de Jong
This work is focused on the process system modelling of an indirectly heated gasifier (10 MWth) using torrefied wood as feedstock and its integration with methanol and power production using Aspen Plus®. The modelling of the gasification process along with the obtained reaction kinetics were validated with experimental data found in literature. Different processing steps such as gasification, gas cleaning and upgrading, methanol synthesis and energy conversion, were modelled and their performance was optimized through a series of sensitivity studies. The results obtained were then used to investigate the effect of different technologies and the variation of operational parameters on the overall process performance. Three cases were examined: “syngas production” (case 1), “methanol production” (case 2), and “power production” (IGCC) (case 3). Case 1 and case 2 were simulated using sand and dolomite as bed materials respectively, in order to study the incorporation of Absorption Enhanced Reforming (AER) on the syngas and methanol production efficiency. For case 3 the simulation was performed for two different configurations: a conventional Integrated Gasification Combined Cycle (IGCC) and an innovative Inverted Brayton Cycle (IBC) turbine system. Dolomite was used as the bed material for both configurations. For case 1, an increase of 5% in hydrogen yield in the product gas when AER is applied was observed. For case 2, higer values of Cold Gas Efficiency and Net Efficiency (34% and 60% instead of 33% and 55%, respectively) and a slightly lower value of Carbon Conversion (96% instead of 100%) were obtained when AER was employed. Gasification temperature was lowered by 110 °C in this scenario. For case 3, a lower value of Net Efficiency was obtained when IBC was considered (43% instead of 47%), while a value of 60% was obtained for methanol production with AE. Moreover, the results of case 3, showed that the latent heat in the hot syngas is best utilised when IBC is considered. The developed model accurately predicted the composition of the produced gas and the operational conditions of all the identified blocks within the methanol synthesis and power production processes. This way the use of this model as a generic tool to compare the utilization of different technologies on the performance of the overall process was validated. ...
This work focused on system modelling regarding the investigation of biomass gasification and its integration with methanol synthesis and power production. Indirect gasification technology was chosen and modelledusing Aspen Plus®. The gasification process along with the reaction kinetics were validated with experimental data from the literature. Different processing steps towards the conversion of biomass to methanol were identified and modelled. The identified process blocks were: Gasifier, Gas Cleaning Unit, Methanol Synthesis and the Energy Conversion Network were optimised by performing a series of sensitivity studies. The optimised model was then used to study the effect of choosing different technologies and parameters within the blocks on the overall process behaviour. Two different case studies were examined, each distinct from each other by a difference in technology of one of the blocks. The two cases concerned the impact of incorporation of Absorption Enhanced Reforming (AER) on the methanol synthesis process, simulated by using dolomite as the bed material. Sankey plots for each of these cases were drawn to visualize the energy losses in such complicated systems. The developed model accurately predicted the composition of the major product gas and operational conditions of all the identified blocks within the methanol synthesis process. The effect of incorporating AER on the overall methanol synthesis process was simulated. The developed model proved its use as a generic tool to compare the effect of different choices in technology on the performance of the overall process. ...