Among the oleaginous plants, Jatropha curcas is a species with considerable potential energy for use as biodiesel. The waste disposal parts of the fruit, after the oil extraction, as well as its epicarp are considered energy loss. Under the conditions presented, this study was aimed to accomplish the energy characterization of the remaining epicarp achieved by the processing of the Jatropha fruit, along with briquetes. It was attested through the proximate analysis of materials and particle size distribution, heating value determination, briquettes production and mechanical testing. The treatments consisted of briquettes with different amounts of particle materials of Jatropha and Pine Wood. The resulting values of chemical analysis, as well as ash content (7.71%), fixed carbon content (26.73%), volatile matter (65.56%) and high heating value (HHV) (4079,24 kcal.kg -1), indicate the quality of the epicarp for energy use. The ash content values were relatively low when compared to other residual materials. The addition of pine wood to Jatropha briquette represented an improvement of the mechanical characteristics in all of the tested proportions. However, all the treatments results in low values of resistance.

The aim of this study was to apply geostatistics to predict the spatial pattern of variations in moisture content of eucalyptus wood chip piles to generate subsidies for adequate sampling and material handling. Wood chip piles were installed in three different storage cycles using newly cut material for 7, 30, and 60 days. Sampling was performed in axial (top, middle, and base) and lateral positions, so that all samples were georeferenced in relation to the distance from the ground and the center of the pile. Moisture values were submitted to geostatistical analysis and kriging. The results confirmed the spatial dependence of moisture content over the piles stored at 7 and 30 days; however, the effect of rainfall was crucial to reduce the spatial dependence of moisture content. The results showed that geostatistics is a useful tool for the creation of reliable sampling protocols.