Energy Optimization of Sugarcane Bagasse by Oxidative Torrefaction: A Multiple Linear Regression Method

Abstract

Biomass is widely recognized as a promising substitute for fossil fuels due to zero CO2 emissions, global availability, storage capacity, and immediate response to demand. Therefore, this research aimed to develop and apply a multiple linear regression model to predict the calorific value in oxidative torrefied sugarcane bagasse. An innovative method was used to enhance the efficiency of torrefaction process, focusing on predicting the calorific value through temperature and oxygen concentration. Detailed analyses of collected data were carried out in the RStudio software environment, which showed the capacity of the model to explain calorific value of sugarcane bagasse, achieving a coefficient of determination R2 of 88.29%. The results showed that the model enhanced the understanding of biomass torrefaction processes and provided valuable tools for optimization, promoting more efficient and sustainable practices in energy generation from agricultural residues such as sugarcane bagasse. The novelty of this research was in presenting a specific and rigorous method to address a significant challenge in the field of renewable energy, offering tangible results that could have a significant impact on the industry.