| Popis: |
The future of conventional fuels has limited sustainability and creates disquietude because of the ubiquitous energy crisis worldwide. The judicious use of biomass or wood-based fuels is inevitable. The quality of wood fuels depends on the moisture content, and subsequently, solar drying solutions can play a vital role in adequately storing and controlling moisture in the fuels. In the present study, a novel forced convection cabinet-type solar dryer was developed and investigated for its thermal performance. An artificial neural network (ANN model) was created to predict the final moisture content of the drying system. The drying behavior of three distinct wood fuels, i.e., woodchips, sawdust, and pellets, was kept under observation to plot the drying curve based on their calculated moisture ratio. The dryer reached a maximum temperature of 60 °C while maintaining a temperature gradient of 10–20 °C. The maximum thermal energy and exergy efficiency was recorded as 55% and 51.1%, respectively. The ANN-optimized model was found suitable with reasonable values of coefficient of correlation (R) for the model. |
