Prediction of Calorific Value of Biomass from Proximate Analysis

Ozyuguran A., Yaman S.

3rd International Conference on Energy and Environment Research (ICEER), Barcelona, Spain, 7 - 11 September 2016, vol.107, pp.130-136 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 107
  • Doi Number: 10.1016/j.egypro.2016.12.149
  • City: Barcelona
  • Country: Spain
  • Page Numbers: pp.130-136
  • Keywords: Biomass, calorific value, proximate analysis, prediction, correlation, ELEMENTAL COMPOSITION
  • Istanbul Technical University Affiliated: Yes


Biomass is one of the renewable and sustainable energy sources that does not lead greenhouse gas emissions. Efficient use of biomass energy will help to solve problems resulting from fossil fuels. However, the main concern relevant to use of this energy is mainly related to low calorific value of biomass. Therefore, calorific value is the key parameter to evaluate the fuel quality of a special biomass material in energetic applications. In this context, twenty-seven different biomass species that represent very wide range of biomass materials such as herbaceous and woody biomasses, nut shells, fruit stones, stem and husks, pulps, and agricultural residues have been characterized by proximate analysis (moisture, volatile matter, fixed carbon, and ash contents). Then, various empirical equations which contain linear and nonlinear terms have been tested in order to predict the higher heating values (HHV) of full sample set from the proximate analysis results. It was concluded that since biomasses used in this study have different structures and fuel characteristics, the predicted HHVs for the full sample set were a bit different from the experimental HHVs and the r2 of these equations varied in the range of 0.812-0.837, while standard deviations were between 1.469 and 1.493 MJ/kg. Nevertheless, considering the number of the biomass species used in this study and their differences in properties, these standard deviations may be regarded in the acceptable limits. (C) 2017 The Authors. Published by Elsevier Ltd.