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Ingeniería e Investigación

Print version ISSN 0120-5609

Abstract

VILLEGAS, J. M  and  AVILA, H. Quick-scan estimating model of Higher Heating Value of oil palm empty fruit bunches based on ash from proximate analysis data. Ing. Investig. [online]. 2014, vol.34, n.2, pp.33-38. ISSN 0120-5609.  http://dx.doi.org/10.15446/ing.investig.v34n2.40504.

A correlation model developed for the quick-scan estimation of the Higher Heating Value (HHV) based on the ash content from a proximate analysis of Oil Palm Empty Fruit Bunches (EFB) is presented in this paper. The correlation was developed using a best subsets regression method with data of biomass samples. EFB were taken directly from the end of the processing line (the process exit after the fruit removal section) in agro-industrial palm oil extraction facilities that are located on the Colombian coast. The correlation is also compared with other published correlations of lignocellulosic biomass. After conducting a statistical analysis from proximate analysis variables expressed in the dry basis for Fixed Carbon (FC), Ash content (Ash), and Volatile Matter (VM), colinearity was identified between Ash - VM - FC, VM - FC, in developed models that show unsatisfactory behavior when these variables are included, indicating that these models are inadequate. Finally, the correlation model for a quick-scan estimation on the dry basis was obtained based on the Ash content from a proximate analysis of EFB (HHV= 0.827Ash + C, with C between 9.97 and 12.4), with a mean absolute error (MEA) lower than 3% and a marginal mean bias error (MBE) of 0.19%, and R2 = 0.8, indicating that the model has an HHV with single input variable predictive capability. This model can be used as a support tool for quick-scan estimation when evaluating the available bioenergy in the processing of EFB using economical and efficient energetic indicators. The minimum and maximum values of HHV obtained for EFB were 13.6 and 21.91 MJ/kg, respectively.

Keywords : Oil Palm Empty Fruit Bunch (EFB); Higher Heating Value; Proximate analysis; Lignocellulosic Biomass; Biofuel energy.

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