Research Papers: Fuel Combustion

Which One Does Better Predict the Heating Value of Biomass?—Dry Based or As-Received Based Proximate Analysis Results?

[+] Author and Article Information
A. Ozyuguran

Chemical and Metallurgical Engineering Faculty, Department of Chemical Engineering,
Istanbul Technical University,
Maslak 34469, Istanbul, Turkey
e-mail: kmayse@itu.edu.tr

H. Haykiri-Acma

Chemical and Metallurgical Engineering Faculty, Department of Chemical Engineering,
Istanbul Technical University,
Maslak 34469, Istanbul, Turkey
e-mail: hanzade@itu.edu.tr

S. Yaman

Chemical and Metallurgical Engineering Faculty, Department of Chemical Engineering,
Istanbul Technical University,
Maslak 34469, Istanbul, Turkey
e-mail: yamans@itu.edu.tr

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the Journal of Energy Resources Technology. Manuscript received January 21, 2019; final manuscript received April 22, 2019; published online May 14, 2019. Assoc. Editor: Samer F. Ahmed.

J. Energy Resour. Technol 141(11), 112202 (May 14, 2019) (7 pages) Paper No: JERT-19-1040; doi: 10.1115/1.4043638 History: Received January 21, 2019; Accepted April 28, 2019

Thirty-nine different species of waste biomass materials that include woody or herbaceous resources as well as nut shells and juice pulps were used to develop empirical equations to predict the calorific value based on the proximate analysis results. Ten different linear/nonlinear equations that contain proximate analysis ingredients including or excluding the moisture content were tested by means of least-squares method to predict the HHV (higher heating value). Prediction performance of each equation was evaluated considering the experimental and the predicted values of HHV and the criteria of MAE (mean absolute error), AAE (average absolute error), and ABE (average bias error). It was concluded that the presence of moisture as a parameter improves the prediction performance of these equations. Also, the samples were classified into two subsets according to their fixed carbon (FC)/ash values and then the correlations were repeated for each subset. Both the full set of samples and the subsets showed a similar trend that the presence of moisture in equations enhances the prediction performance. Also, the FC content may be disregarded from the equation of the calorific value prediction when the FC/ash ratio is lower than a given value.

Copyright © 2019 by ASME
Topics: Biomass , Heating , Carbon
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Grahic Jump Location
Fig. 1

Ternary diagram for fixed carbon, ash, and volatile matter contents

Grahic Jump Location
Fig. 2

Classification of the samples into subsets on dry basis

Grahic Jump Location
Fig. 3

Correlations for subsets



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