Abstract

RODRIGUEZ-ROMERO, Luis Antonio; GUTIERREZ-ANTONIO, Claudia; GARCIA-TREJO, Juan Fernando  and  FEREGRINO-PEREZ, Ana Angélica. Comparative Study of Mathematical Models to Predict the Calorific Value of Mexican Agricultural Wastes. TecnoL. [online]. 2022, vol.25, n.53, e200.  Epub Aug 03, 2022. ISSN 0123-7799.  https://doi.org/10.22430/22565337.2142.

Agricultural residues represent a pollution problem because they are inadequately disposed of and high volumes of these wastes are generated. Therefore, revaluating them to produce biofuels is attractive, but, for that purpose, their calorific value should be established. Some mathematical models reported in the literature to predict calorific value have considered elemental, structural, and proximal analyses, the latter being the least expensive type. This article compares different mathematical models that have been used to predict calorific value based on elemental analysis in order to 1) evaluate agricultural residues from Mexico (bean straw, wheat straw, rice husks, and coffee husks) and other residues reported in the literature (coconut fibers and husks, garden waste, canola hulls, Jatropha curcas husks, and wheat straw) and 2) determine if the existing models work adequately for Mexican biomasses. Thus, Mexican biomasses were characterized using proximal analyses, and the calorific value of all the biomasses was estimated employing previously reported linear mathematical models. The results, which were compared with experimental values, show that the coefficients of determination of the existing mathematical models are low, particularly when Mexican biomass data are used. The best model to predict the calorific value of Mexican agricultural residues (R2 = 0.72) considers only the content of volatile matter and fixed carbon, in addition to a weak functionality of the ash content. Consequently, mathematical models should be proposed specifically for Mexican biomass.

Keywords : Renewable energy sources; biomass; calorific value; proximal analysis; predictive model.

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