PREDICTING HIGHER HEATING VALUE OF AGRO-INDUSTRIAL WASTE: CLASSIFICATION AND MODELING BASED ON PROXIMATE AND ULTIMATE ANALYSIS

Abstract

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.

Keywords

• biomass energy
• higher heating value
• proximate analysis
• ultimate analysis