The regression analysis of fast pyrolysis product yields and determination of product quality


ATEŞ F., ERGİNEL N.

FUEL, vol.102, pp.681-690, 2012 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 102
  • Publication Date: 2012
  • Doi Number: 10.1016/j.fuel.2012.05.051
  • Journal Name: FUEL
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.681-690
  • Keywords: Sawdust, Fast pyrolysis, Regression model, GC/MS, BIOMASS SAMPLES, TEMPERATURE, REACTOR, OIL, FUELS, SIZE
  • Anadolu University Affiliated: Yes

Abstract

The main objective of this study was to investigate the influence of pyrolysis parameters on product yields with experimental and to evaluate of results with the regression models. For this purpose; horn-beam sawdust was used as a biomass sample in the experiments. Variables investigated were pyrolysis temperature between 300 and 800 degrees C and heating rates between 50 and 700 degrees C/min. The experimental results indicated that a maximum bio-oil yield of 40.4% was obtained at a heating rate of 500 degrees C/min and pyrolysis temperature of 600 degrees C. The experimental results have been analyzed with several linear and nonlinear regression models. As a results; logarithmic + quadratic models with R-2(adj.) = 80.8 and 91 are the best fit regression models to the bio-char and bio-oil yield as a function of heating rate, respectively. Also, regression models were investigated with bio-oil yield and bio-char yield as a dependent variables and temperature as independent variable. The best fit regression model for the relationship between bio-oil yield and temperature has been identified as quadratic model with R-2(adj.) = 92.7; while for the relationship between bio-char yield and temperature with the R-2(adj.) = 92.9 is logarithmic model. The chemical characteristics of the bio-oil and bio-char product were identified by elemental and calorific analyses. The bio-oils were then analyzed using GC/MS technique. (C) 2012 Elsevier Ltd. All rights reserved.