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

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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.

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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

References

  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. https://doi.org/10.1016/j.proci.2016.08.086

    Article  Google Scholar 

  2. Saxena RC, Adhikari DK, Goyal HB (2009) Biomass-based energy fuel through biochemical routes: a review. Renew Sust Energ Rev 13:167–178. https://doi.org/10.1016/j.rser.2007.07.011

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.apenergy.2012.05.012

    Article  Google Scholar 

  4. Demirbas A (1997) Calculation of higher heating values of biomass fuels. Fuel 76:431–434. https://doi.org/10.1016/S0016-2361(97)85520-2

    Article  Google Scholar 

  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. https://doi.org/10.1080/19392699.2010.534683

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.fuel.2016.04.051

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.biortech.2017.03.015

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.fuel.2013.08.064

    Article  Google Scholar 

  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. https://doi.org/10.1016/S0016-2361(01)00034-5

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.fuproc.2016.06.040

    Article  Google Scholar 

  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. https://doi.org/10.1016/S0016-2361(03)00009-7.

    Article  Google Scholar 

  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 8:767–771

    Google Scholar 

  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. https://doi.org/10.1016/j.proeng.2015.11.048

    Article  Google Scholar 

  14. Sheng C, Azevedo JLTÃ (2005) Estimating the higher heating value of biomass fuels from basic analysis data. Biomass Bioenergy 28:499–507. https://doi.org/10.1016/j.biombioe.2004.11.008

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.fuel.2005.01.003

    Article  Google Scholar 

  16. Yin C-Y (2011) Prediction of higher heating values of biomass from proximate and ultimate analyses. Fuel 90:1128–1132. https://doi.org/10.1016/j.fuel.2010.11.031

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.indcrop.2015.09.078

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.fuel.2013.08.023

    Article  Google Scholar 

  19. Channiwala SA, Parikh PP (2002) A unified correlation for estimating HHV of solid, liquid and gaseous fuels. Fuel 81:1051–1063. https://doi.org/10.1016/S0016-2361(01)00131-4

    Article  Google Scholar 

  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:https://doi.org/10.1016/j.rser.2012.02.054

  21. Tillman DA (1978) Wood as an energy resource. Academic press. Inc, Cambridge

    Google Scholar 

  22. Jenkins B (1980) Downdraft gasification characteristics of mayor California residue derived fuels. PhD Thesis. Univ California, Davis.

  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: 371

  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.

  25. Demirbas A, Gullu D, Çaglar A, Akdeniz F (1997) Estimation of calorific values of fuels from lignocellulosics. Energy Sources 19:765–770. https://doi.org/10.1080/00908319708908888

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.rser.2010.11.012

    Article  Google Scholar 

  27. García R, Pizarro C, Lavín AG, Bueno JL (2014) Spanish biofuels heating value estimation. Part I: ultimate analysis data. Fuel 117:1130–1138. https://doi.org/10.1016/j.fuel.2013.08.048

    Article  Google Scholar 

  28. Demirbas A, Demirbas AH (2004) Estimating the calorific values of lignocellulosic fuels. Energy Explor Exploit 22:135–143. https://doi.org/10.1260/01445980414751988

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.wasman.2016.11.012.

    Article  Google Scholar 

  30. García R, Pizarro C, Lavín AG, Bueno JL (2017) Biomass sources for thermal conversion. Technoeconomical overview. Fuel 195:182–189. https://doi.org/10.1016/j.fuel.2017.01.063

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.scitotenv.2015.11.017

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.biortech.2013.08.135

    Article  Google Scholar 

  33. Wiriyaumpaiwong S, Jamradloedluk J (2009) Biomass fired grate boiler for small industrial heating system. Proceeding ISES World Congr 2007.

  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. https://doi.org/10.1016/j.renene.2009.08.033

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.renene.2016.09.072

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.fuel.2014.04.061

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.biortech.2012.06.091

    Article  Google Scholar 

  38. Debdoubi A, El Amarti A, Colacio E (2005) Production of fuel briquettes from esparto partially pyrolyzed. Energy Convers Manag 46:1877–1884. https://doi.org/10.1016/j.enconman.2004.09.005

    Article  Google Scholar 

  39. Vamvuka D, Kakaras E, Kastanaki E, Grammelis P (2003) Pyrolysis characteristics and kinetics of biomass residuals mixtures with lignite. Fuel 82:1949–1960. https://doi.org/10.1016/S0016-2361(03)00153-4

    Article  Google Scholar 

  40. Bonelli PR (2003) Slow pyrolysis of nutshells: characterization of derived chars and of process kinetics. Energy Sources 25:767–778. https://doi.org/10.1080/00908310390207819

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.fuel.2006.07.026

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.apenergy.2016.05.088

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.biortech.2016.04.009

    Article  Google Scholar 

  44. Miranda MT, Arranz JI, Rojas S, Montero I (2009) Energetic characterization of densified residues from Pyrenean oak forest. Fuel 88:2106–2112. https://doi.org/10.1016/j.fuel.2009.05.015

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.ijhydene.2007.12.051

    Article  Google Scholar 

  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. https://doi.org/10.1080/00986440802300984

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.jaap.2015.08.005

    Article  Google Scholar 

  48. Haykiri-Acma H, Yaman S (2009) Effect of biomass on burnouts of Turkish lignites during co-firing. Energy Convers Manag 50:2422–2427. https://doi.org/10.1016/j.enconman.2009.05.026

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.biombioe.2016.07.004

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.biortech.2014.12.048

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.joei.2016.03.002

    Article  Google Scholar 

  52. Forouzanfar M, Doustmohammadi A, Hasanzadeh S, Shakouri GH (2012) Transport energy demand forecast using multi-level genetic programming. Appl Energy 91:496–503. https://doi.org/10.1016/j.apenergy.2011.08.018

    Article  Google Scholar 

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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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Correspondence to Imane Boumanchar.

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Boumanchar, I., Charafeddine, K., Chhiti, Y. et al. Biomass higher heating value prediction from ultimate analysis using multiple regression and genetic programming. Biomass Conv. Bioref. 9, 499–509 (2019). https://doi.org/10.1007/s13399-019-00386-5

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