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


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.


Higher heating value HHV prediction Multiple variable regression Genetic programming 



Average absolute error


Average bias error




Correlation coefficient


Computational intelligence




Higher heating value


Gross heating value


Genetic programming


Lower heating value


Multiple variable regression analysis




Net heating value







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


  1. 1.
    Hu Y, Wang S, Wang Q, He Z, Lin X, Xu S, Ji H, Li Y (2017) Effect of different pretreatments on the thermal degradation of seaweed biomass. Proc Combust Inst 36:2271–2281. CrossRefGoogle Scholar
  2. 2.
    Saxena RC, Adhikari DK, Goyal HB (2009) Biomass-based energy fuel through biochemical routes: a review. Renew Sust Energ Rev 13:167–178. CrossRefGoogle Scholar
  3. 3.
    Srirangan K, Akawi L, Moo-Young M, Chou CP (2012) Towards sustainable production of clean energy carriers from biomass resources. Appl Energy 100:172–186. CrossRefGoogle Scholar
  4. 4.
    Demirbas A (1997) Calculation of higher heating values of biomass fuels. Fuel 76:431–434. CrossRefGoogle Scholar
  5. 5.
    Erik NY, Yilmaz I (2011) On the use of conventional and soft computing models for prediction of gross calorific value (GCV) of coal. Int J Coal Prep Util 1010:32–59. CrossRefGoogle Scholar
  6. 6.
    Estiati I, Freire FB, Freire JT, Aguado R, Olazar M (2016) Fitting performance of artificial neural networks and empirical correlations to estimate higher heating values of biomass. Fuel 180:377–383. CrossRefGoogle Scholar
  7. 7.
    Uzun H, Yıldız Z, Goldfarb JL, Ceylan S (2017) Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis. Bioresour Technol 234:122–130. CrossRefGoogle Scholar
  8. 8.
    Choi HL, Sudiarto SIA, Renggaman A (2014) Prediction of livestock manure and mixture higher heating value based on fundamental analysis. Fuel 116:772–780. CrossRefGoogle Scholar
  9. 9.
    Cordero T, Marquez F, Rodriguez-Mirasol J, Rodriguez J (2001) Predicting heating values of lignocellulosics and carbonaceous materials from proximate analysis. Fuel 80:1567–1571. CrossRefGoogle Scholar
  10. 10.
    Hosokai S, Matsuoka K, Kuramoto K, Suzuki Y (2016) Modification of Dulong’s formula to estimate heating value of gas, liquid and solid fuels. Fuel Process Technol 152:399–405. CrossRefGoogle Scholar
  11. 11.
    Kathiravale S, Noor M, Yunus M, Sopian K, Samsuddin AH, Rahman RA (2003) Modeling the heating value of municipal solid waste. Fuel 82:1119–1125. CrossRefGoogle Scholar
  12. 12.
    Kricka T, Voca N, Savic TB, Bilandzija N, Sito S (2010) Higher heating values estimation of horticultural biomass from their proximate and ultimate analyses data. J Food, Agric Environ 8:767–771Google Scholar
  13. 13.
    Setyawati W, Damanhuri E, Lestari P, Dewi K (2016) Correlation equation to predict HHV of tropical peat based on its ultimate analyses. Procedia Eng 125:298–303. CrossRefGoogle Scholar
  14. 14.
    Sheng C, Azevedo JLTÃ (2005) Estimating the higher heating value of biomass fuels from basic analysis data. Biomass Bioenergy 28:499–507. CrossRefGoogle Scholar
  15. 15.
    Thipkhunthod P, Meeyoo V, Rangsunvigit P, Kitiyanan B, Siemanond K, Rirksomboon T (2005) Predicting the heating value of sewage sludges in Thailand from proximate and ultimate analyses. Fuel 84:849–857. CrossRefGoogle Scholar
  16. 16.
    Yin C-Y (2011) Prediction of higher heating values of biomass from proximate and ultimate analyses. Fuel 90:1128–1132. CrossRefGoogle Scholar
  17. 17.
    Álvarez A, Pizarro C, García R, Bueno JL (2015) Spanish biofuels heating value estimation based on structural analysis. Ind Crop Prod 77:983–991. CrossRefGoogle Scholar
  18. 18.
    Callejón-ferre AJ, Carreño-sánchez J, Suárez-medina FJ, Pérez-alonso J, Velázquez-martí B (2014) Prediction models for higher heating value based on the structural analysis of the biomass of plant remains from the greenhouses of Almería (Spain). Fuel 116:377–387. CrossRefGoogle Scholar
  19. 19.
    Channiwala SA, Parikh PP (2002) A unified correlation for estimating HHV of solid, liquid and gaseous fuels. Fuel 81:1051–1063. CrossRefGoogle Scholar
  20. 20.
    Vargas-moreno JM, Callejón-ferre AJ, Pérez-alonso J, Velázquez-martí B (2012) A review of the mathematical models for predicting the heating value of biomass materials;16:3065–83. doi:
  21. 21.
    Tillman DA (1978) Wood as an energy resource. Academic press. Inc, CambridgeGoogle Scholar
  22. 22.
    Jenkins B (1980) Downdraft gasification characteristics of mayor California residue derived fuels. PhD Thesis. Univ California, Davis.Google Scholar
  23. 23.
    Jenkins B, Ebeling J (1985) Correlation of physical and chemical properties of terrestrial biomass with conversion: symposium energy from biomass and waste IX IGT: 371Google Scholar
  24. 24.
    Beckman D, Elliot D, Gevert B, Hornell C, Kjellstrom B, A O. Techno-economic assessment of selected biomass liquefaction process (VTT research report 697). Espoo VTT Tech Res Cent Finl 1990.Google Scholar
  25. 25.
    Demirbas A, Gullu D, Çaglar A, Akdeniz F (1997) Estimation of calorific values of fuels from lignocellulosics. Energy Sources 19:765–770. CrossRefGoogle Scholar
  26. 26.
    Callejón-Ferre AJ, Velázquez-Martí B, López-Martínez JA, Manzano-Agugliaro F (2011) Greenhouse crop residues: energy potential and models for the prediction of their higher heating value. Renew Sust Energ Rev 15:948–955. CrossRefGoogle Scholar
  27. 27.
    García R, Pizarro C, Lavín AG, Bueno JL (2014) Spanish biofuels heating value estimation. Part I: ultimate analysis data. Fuel 117:1130–1138. CrossRefGoogle Scholar
  28. 28.
    Demirbas A, Demirbas AH (2004) Estimating the calorific values of lignocellulosic fuels. Energy Explor Exploit 22:135–143. CrossRefGoogle Scholar
  29. 29.
    Boumanchar I, Chhiti Y, Ezzahrae F, Alaoui M, El A, Sahibed-dine A et al (2016) Effect of materials mixture on the higher heating value: case of biomass, biochar and municipal solid waste. Waste Manag 61:78–86. CrossRefGoogle Scholar
  30. 30.
    García R, Pizarro C, Lavín AG, Bueno JL (2017) Biomass sources for thermal conversion. Technoeconomical overview. Fuel 195:182–189. CrossRefGoogle Scholar
  31. 31.
    Jiang Y, Ameh A, Lei M, Duan L, Longhurst P (2016) Solid–gaseous phase transformation of elemental contaminants during the gasification of biomass. Sci Total Environ 563–564:724–730. CrossRefGoogle Scholar
  32. 32.
    Lee Y, Park J, Ryu C, Seop K, Yang W, Park Y et al (2013) Comparison of biochar properties from biomass residues produced by slow pyrolysis at 500 °C. Bioresour Technol 148:196–201. CrossRefGoogle Scholar
  33. 33.
    Wiriyaumpaiwong S, Jamradloedluk J (2009) Biomass fired grate boiler for small industrial heating system. Proceeding ISES World Congr 2007.Google Scholar
  34. 34.
    Naik S, Goud VV, Rout PK, Jacobson K, Dalai AK (2010) Characterization of Canadian biomass for alternative renewable biofuel. Renew Energy 35:1624–1631. CrossRefGoogle Scholar
  35. 35.
    Luo S, Fu J, Zhou Y, Yi C (2017) The production of hydrogen-rich gas by catalytic pyrolysis of biomass using waste heat from blast-furnace slag. Renew Energy 101:1030–1036. CrossRefGoogle Scholar
  36. 36.
    Poddar S, Kamruzzaman M, Sujan SMA, Hossain M, Jamal MS, Gafur MA (2014) Effect of compression pressure on lignocellulosic biomass pellet to improve fuel properties: higher heating value. Fuel 131:43–48. CrossRefGoogle Scholar
  37. 37.
    Ren S, Lei H, Wang L, Bu Q, Chen S, Wu J, Julson J, Ruan R (2013) The effects of torrefaction on compositions of bio-oil and syngas from biomass pyrolysis by microwave heating. Bioresour Technol 135:659–664. CrossRefGoogle Scholar
  38. 38.
    Debdoubi A, El Amarti A, Colacio E (2005) Production of fuel briquettes from esparto partially pyrolyzed. Energy Convers Manag 46:1877–1884. CrossRefGoogle Scholar
  39. 39.
    Vamvuka D, Kakaras E, Kastanaki E, Grammelis P (2003) Pyrolysis characteristics and kinetics of biomass residuals mixtures with lignite. Fuel 82:1949–1960. CrossRefGoogle Scholar
  40. 40.
    Bonelli PR (2003) Slow pyrolysis of nutshells: characterization of derived chars and of process kinetics. Energy Sources 25:767–778. CrossRefGoogle Scholar
  41. 41.
    Miranda MT, Cabanillas A, Rojas S, Montero I, Ruiz A (2007) Combined combustion of various phases of olive wastes in a conventional combustor. Fuel 86:367–372. CrossRefGoogle Scholar
  42. 42.
    Corton J, Donnison IS, Patel M, Bühle L, Hodgson E, Wachendorf M, Bridgwater A, Allison G, Fraser MD (2016) Expanding the biomass resource: sustainable oil production via fast pyrolysis of low input high diversity biomass and the potential integration of thermochemical and biological conversion routes. Appl Energy 177:852–862. CrossRefGoogle Scholar
  43. 43.
    Bach Q, Chen W, Chu Y, Skreiberg Ø (2016) Predictions of biochar yield and elemental composition during torrefaction of forest residues. Bioresour Technol 215:239–246. CrossRefGoogle Scholar
  44. 44.
    Miranda MT, Arranz JI, Rojas S, Montero I (2009) Energetic characterization of densified residues from Pyrenean oak forest. Fuel 88:2106–2112. CrossRefGoogle Scholar
  45. 45.
    Skoulou V, Zabaniotou A, Stavropoulos G, Sakelaropoulos G (2008) Syngas production from olive tree cuttings and olive kernels in a downdraft fixed-bed gasifier. Int J Hydrog Energy 33:1185–1194. CrossRefGoogle Scholar
  46. 46.
    Demiral İ, Atilgan NG, Şensöz S (2008) Production of biofuel from soft shell of pistachio (Pistacia vera L.). Chem Eng Commun 196:104–115. CrossRefGoogle Scholar
  47. 47.
    Wei L, Liang S, Guho NM, Hanson AJ, Smith MW, Garcia-perez M et al (2015) Production and characterization of bio-oil and biochar from the pyrolysis of residual bacterial biomass from a polyhydroxyalkanoate production process. J Anal Appl Pyrolysis 115:268–278. CrossRefGoogle Scholar
  48. 48.
    Haykiri-Acma H, Yaman S (2009) Effect of biomass on burnouts of Turkish lignites during co-firing. Energy Convers Manag 50:2422–2427. CrossRefGoogle Scholar
  49. 49.
    Solar J, De MI, Caballero BM, Rodriguez N, Agirre I, Adrados A (2016) Influence of temperature and residence time in the pyrolysis of woody biomass waste in a continuous screw reactor. Biomass Bioenergy 95:416–423. CrossRefGoogle Scholar
  50. 50.
    Shankar D, Pan I, Das S, Leahy JJ, Kwapinski W (2015) Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier. Bioresour Technol 179:524–533. CrossRefGoogle Scholar
  51. 51.
    Ghugare SB, Tambe SS (2016) Genetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies. J Energy Inst 90:476–484. CrossRefGoogle Scholar
  52. 52.
    Forouzanfar M, Doustmohammadi A, Hasanzadeh S, Shakouri GH (2012) Transport energy demand forecast using multi-level genetic programming. Appl Energy 91:496–503. CrossRefGoogle Scholar

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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