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Biomass higher heating value prediction from ultimate analysis using multiple regression and genetic programming

  • Imane BoumancharEmail author
  • Kenza Charafeddine
  • Younes Chhiti
  • Fatima Ezzahrae M’hamdi Alaoui
  • Abdelaziz Sahibed-dine
  • Fouad Bentiss
  • Charafeddine Jama
  • Mohammed Bensitel
Original Article
  • 10 Downloads

Abstract

The higher heating value (HHV) is a significant parameter for the determination of fuel quality. However, its measurement is time-consuming and requires sophisticated equipment. For this reason, several researches have been interested to develop mathematical models for the prediction of HHV from fundamental composition. The purpose of this study is to develop new correlations to determine the biomass HHV from ultimate analysis. As a result, two models were elaborated. The first was developed using multiple variable regression analysis while the second has adopted genetic programming formalism. Data of 171 from various types of biomass samples were randomly used for the development (75%) and the validation (25%) of new equations. The accuracy of the established models was compared to previous literature works in terms of correlation coefficient (CC), average absolute error (AAE), and average bias error (ABE). The proposed models were more performing with the highest CC and the smallest errors.

Keywords

Higher heating value HHV prediction Multiple variable regression Genetic programming 

Abbreviations

AAE

Average absolute error

ABE

Average bias error

C

Carbon

CC

Correlation coefficient

CI

Computational intelligence

H

Hydrogen

HHV

Higher heating value

GHV

Gross heating value

GP

Genetic programming

LHV

Lower heating value

MVRA

Multiple variable regression analysis

N

Nitrogen

NHV

Net heating value

O

Oxygen

S

Sulfur

Notes

Acknowledgements

Laboratory of Catalysis and Corrosion of Materials (LCCM), Science Engineer Laboratory for Energy (LabSIPE), and UMET (Unité matériaux et transformations) laboratory are gratefully thanked.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Imane Boumanchar
    • 1
    • 2
    Email author
  • Kenza Charafeddine
    • 3
  • Younes Chhiti
    • 2
  • Fatima Ezzahrae M’hamdi Alaoui
    • 2
  • Abdelaziz Sahibed-dine
    • 1
  • Fouad Bentiss
    • 1
  • Charafeddine Jama
    • 4
  • Mohammed Bensitel
    • 1
  1. 1.Laboratory of Catalysis and Corrosion of Materials (LCCM), Chemistry DepartmentChouaïb Doukkali UniversityEl JadidaMorocco
  2. 2.Science Engineer Laboratory for Energy (LabSIPE), National School of Applied SciencesChouaïb Doukkali UniversityEl JadidaMorocco
  3. 3.ANISSE Team, Faculty of SciencesMohammed V UniversityRabatMorocco
  4. 4.Lille UniversityLilleFrance

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