No abstract

Cashew Nut Shell (CNS), an abundant waste from the cashew nut production, could become a promising material for gasification. Nevertheless, engineering data regarding this technology is still small and fragmented. In this study, the evolution of the char textural properties during gasification, as well as the effect of operating conditions on char gasification kinetics were extensively investigated and quantified. The textural properties of cashew nut shell char changed significantly following different pathways between CO2 and H2O gasification. Micropores and mesopores were well developed along with the conversion in the case of H2O gasification instead of only micropores in the case of CO2 gasification. Regarding char gasification kinetics, an increase of the temperature from 800 to 1000°C enhanced five times the conversion rate under an H2O atmosphere and 15 times under a CO2 atmosphere. The conversion rate increased two times when the concentration of reacting gases changed from 20 to 60 vol % for both atmospheres. Similarities in terms of gasification kinetics between CNS chars and wood chip chars were found in this range of operating conditions.

No abstract

Sewage sludge hydrochars (SSHs), which are produced by hydrothermal carbonization (HTC), offer a high calorific value to be applied as a biofuel. However, HTC is a complex processand the properties of the resulting product depend heavily on the process conditions and feedstock composition. In this work, we have applied artificial neural networks (ANNs) to contribute to the production of tailored SSHs for a specific application and with optimum properties. We collected data from the published literature covering the years 2014–2021, which was then fed into different ANN models where the input data (HTC temperature, process time, and the elemental content of hydrochars) were used to predict output parameters ((higher heating value, (HHV) and solid yield (%)). The proposed ANN models were successful in accurately predicting both HHV and contents of C and H. While the model NN1 (based on C, H, O content) exhibited HHV predicting performance with R2 = 0.974, another model, NN2, was also able to predict HHV with R2 = 0.936 using only C and H as input. Moreover, the inverse model of NN3 (based on H, O content, and HHV) could predict C content with an R2 of 0.939.

CO2 activation was performed on hydrothermally treated bamboo for the preparation of an EDLC electrode.