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Issue No.Vol. No. 2 Issue No. 1 - Publication Year 2013

AuthorAbdul Razaque SahitoRasool Bux MaharZuhaib SiddiquiKhan Muhammad Brohi

Article TitleEstimating Calorific Values Of Lignocellulosic Biomass From Volatile And Fixed Solids

From Page No.1

To Page No.6

Release Date25-07-2013

Abstract

The gross caloric value (GCV) is the most important property of any fuel, which shows its energy content. The experimental determinationof GCV of solid fuels is a cost intensive process, as it requires exceptional equipment and high skills. To streamline the task, many equationswere developed in past for determining GCV from proximate and ultimate analysis of solid fuels. In present study two empirical equationswere developed to predict the GCV of lignocellulosic biomass using their volatile solids (VS) and xed solids (FS) with and without ashcontents in specied range of VS (60.84–82.64 %), FS (17.36–39.16 %) and ash (1.59–21.76 %) as percentage dry mass basis. The experimentaldata was analyzed through multiple regression analysis and least square method. The empirical equations were developed and their meanerrors were determined. The developed empirical equations are simpler, economical, less time consuming and are more accurate compare tothe equations based on the proximate analysis. The gross caloric value (GCV) is the most important property of any fuel, which shows its energy content. The experimental determination of GCV of solid fuels is a cost intensive process, as it requires exceptional equipment and high skills. To streamline the task, many equations were developed in past for determining GCV from proximate and ultimate analysis of solid fuels. In present study two empirical equations were developed to predict the GCV of lignocellulosic biomass using their volatile solids (VS) and fixed solids (FS) with and without ash contents in specied range of VS (60.84–82.64 %), FS (17.36–39.16 %) and ash (1.59–21.76 %) as percentage dry mass basis. The experimental data was analyzed through multiple regression analysis and least square method. The empirical equations were developed and their mean errors were determined. The developed empirical equations are simpler, economical, less time consuming and are more accurate compare to the equations based on the proximate analysis.

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