ABSTRACT Coffee stands out worldwide for being widely traded and consumed. However, this consumption generates a large amount of spent coffee grounds (SCGs) that are typically arbitrarily discarded. This biomass has a high heating value and molecular composition that allows for it to be a raw material for generating heat and energy. The search for renewable energy sources should consider the potential of SCGs. Thus, the objective of this study was to estimate the energy potential of SCGs in Brazil. The methodology consisted of searching for data to represent the generation of SCGs in the country and estimate its energy potential. It was found that the amount of SCGs available for power generation through direct combustion was approximately one thousand tons, and the power generation potential was 6.83 TWh/year in Brazil. This energy is sufficient to meet, for example, the total annual demand for public lighting in the country's Southeast. These results are the first steps towards evaluating the potential use of SCGs as an energy source and its environmental benefits. Additionally, this study explored limitations and barriers, providing valuable information for developing routes for using this energy residue and incentives for public policies.

ABSTRACT Bioenergy production relies on resources such as agricultural waste, which has prompted extensive research on biofuels. The heating value is a crucial parameter for evaluating energy sources. This study aims to analyze and propose formulations for estimating the higher heating value (HHV) based on proximate and ultimate chemical analysis of biomass. A database consisting of 142 samples was created, and 14 formulas available in the literature were initially tested. The datasets for each composition type were classified using the k-means algorithm, and the new sample spaces were validated. For proximate analysis data, specific multiple linear regression models were developed for two classes, one with an average of 0.697 and SE of 1.05 MJ kg -1 , and the other with an average of 0.678 and SE of 1.27 MJ kg -1 . For samples with ultimate analysis, a general model was formulated with an average of 0.701 and SE of 1.11 MJ kg -1 . Sample classification for proximate analysis did not significantly affect the fit of models. Considering that proximate analysis is less expensive than ultimate analysis, the proposed method shows promise in optimizing and reducing costs for determining the HHV of biomass for energy production.