Analysis of a Combined Solar Drying System for Wood-Chips, Sawdust, and Pellets

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 degrees C while maintaining a temperature gradient of 10-20 degrees 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.

Automated summary

The future of conventional fuels is uncertain due to limited sustainability and the global energy crisis. The use of biomass or wood-based fuels is becoming inevitable. This study developed a novel forced convection solar dryer and created an artificial neural network model to predict the moisture content of the drying system. Observations on the drying behavior of different wood fuels led to the development of drying curves based on calculated moisture ratios. The dryer achieved a maximum temperature of 60 degrees C and had a temperature gradient of 10-20 degrees C. The thermal energy and exergy efficiency of the dryer were recorded as 55% and 51.1%, respectively. The ANN-optimized model showed reasonable correlation values (R) for the prediction.