Abstract
Hydrogen, H2, is likely to become the key renewable energy carrier for the future. Thermochemical conversion of biomass can be applied to produce H2 involving a variety of conversion technologies, including gasification, that produce a mix of synthesis gas, H2 and CO. It is of paramount importance thus having a consolidated, efficient, versatile and clean technology to produce H2 in a sustainable and continuous manner contributing to current energy policies.
For this work, a bench-scale batch gasifier located at the Tokyo Institute of Technology, Tokyo Tech, was used for the production of syngas from the torrefied woody biomass. These data were inputs for the development of an hybrid model for the prediction of the composition of the producer gas. The time-depending data obtained in the semi-batch gasifier process was composed of three regions (pyrolysis, oxidation and reduction zones) in which recurrent neural networks were combined with physical-based models, whose parameters were conveniently tuned to fit the experimental data.
