Statistical models for predicting the higher heating value of torrefied kesambi leaves

The study investigates the effects of temperature and residence time on the energy density of kesambi leaves through experimental torrefaction, proximate analysis, and response surface methodology with a central composite design (RSM-CCD). The torrefaction process enhances the energy density of kesambi leaves by increasing fixed carbon content while reducing volatile matter. The RSM-CCD models developed in this research are both statistically significant and exhibit robust predictive accuracy for estimating higher heating value (HHV), providing valuable insights into optimal torrefaction conditions. Surface plots effectively illustrate the relationships between HHV, temperature, and residence time, enabling the identification of ideal process parameters. Additionally, a desirability analysis reveals opportunities to enhance correlations between HHV and key measured properties, such as moisture content, ash, and volatile matter. This research makes a significant contribution to understanding and optimising the torrefaction process for kesambi leaves, with practical implications for improving energy density and advancing the development of sustainable biofuel sources. By offering a novel approach to predicting HHV in kesambi leaf-based biofuels, the findings highlight the potential for optimising torrefaction processes to enhance the viability of renewable energy resources. Further research is suggested to refine these predictive models and explore additional factors influencing HHV, aiming to bolster the production of sustainable biofuels.

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Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).