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Research Article

Generalizability of empirical correlations for predicting higher heating values of biomass

, ORCID Icon, ORCID Icon &
Pages 5434-5450 | Received 21 Jul 2023, Accepted 11 Mar 2024, Published online: 11 Apr 2024

References

  • Ahmaruzzaman, M. 2008. Proximate analyses and predicting HHV of chars obtained from cocracking of petroleum vacuum residue with coal, plastics and biomass. Bioresource Technology 99 (11):5043–5050. doi:10.1016/j.biortech.2007.09.021.
  • Aziz, N., H. Mohamed, D. Kania, H. C. Ong, B. S. Zainal, H. Junoh, P. J. Ker, and A. S. Silitonga. 2024. Bioenergy production by integrated microwave-assisted torrefaction and pyrolysis. Renewable and Sustainable Energy Reviews 191:114097. doi:10.1016/j.rser.2023.114097.
  • Boztepe, C., M. Daskin, and A. Erdogan. 2022. Synthesis of magnetic responsive poly (NIPAAm-co-VSA)/Fe3O4 IPN ferrogels and modeling their deswelling and heating behaviors under AMF by using artificial neural networks. Reactive and Functional Polymers 173:105219. doi:10.1016/j.reactfunctpolym.2022.105219.
  • Boztepe, C., M. Daskin, A. Erdogan, and T. Sarici. 2021. Preparation of poly (acrylamide‐co‐2‐acrylamido‐2‐methylpropan sulfonic acid)‐g‐Carboxymethyl cellulose/Titanium dioxide hydrogels and modeling of their swelling capacity and mechanic strength behaviors by response surface method technique. Polymer Engineering & Science 61 (7):2083–96. doi:10.1002/pen.25736.
  • Callejón-Ferre, A., B. Velázquez-Martí, J. A. López-Martínez, and F. Manzano-Agugliaro. 2011. Greenhouse crop residues: Energy potential and models for the prediction of their higher heating value. Renewable and Sustainable Energy Reviews 15 (2):948–55. doi:10.1016/j.rser.2010.11.012.
  • Chai, T., and R. R. Draxler. 2014. Root mean square error (RMSE) or mean absolute error (MAE)?–arguments against avoiding RMSE in the literature. geoscientific Model Development 7 (3):1247–50. doi:10.5194/gmd-7-1247-2014.
  • Channiwala, S., and P. Parikh. 2002. A unified correlation for estimating HHV of solid, liquid and gaseous fuels. Fuel 81 (8):1051–63. doi:10.1016/S0016-2361(01)00131-4.
  • Cordero, T., F. Marquez, J. Rodriguez-Mirasol, and J. J. Rodriguez. 2001. Predicting heating values of lignocellulosics and carbonaceous materials from proximate analysis. Fuel 80 (11):1567–71. doi:10.1016/S0016-2361(01)00034-5.
  • Dashti, A., A. S. Noushabadi, M. Raji, A. Razmi, S. Ceylan, and A. H. Mohammadi. 2019. Estimation of biomass higher heating value (HHV) based on the proximate analysis: Smart modeling and correlation. Fuel 257:115931. doi:10.1016/j.fuel.2019.115931.
  • Demirbas, A. 2002. Relationships between heating value and lignin, moisture, ash and extractive contents of biomass fuels. Energy exploration & exploitation 20 (1):105–111. doi:10.1260/014459802760170420.
  • Demirbas, A. 2004. Combustion characteristics of different biomass fuels. Progress in Energy and Combustion Science 30 (2):219–230. doi:10.1016/j.pecs.2003.10.004.
  • Demirbaş, A. 1997. Calculation of higher heating values of biomass fuels. Fuel 76 (5):431–34. doi:10.1016/S0016-2361(97)85520-2.
  • Demirbas, A., D. Gullu, A. Çaglar, and F. Akdeniz. 1997. Estimation of calorific values of fuels from lignocellulosics. Energy Sources 19 (8):765–70. doi:10.1080/00908319708908888.
  • Dobbelaere, M. R., P. P. Plehiers, R. Van de Vijver, C. V. Stevens, and K. M. Van Geem. 2021. Machine learning in chemical engineering: Strengths, weaknesses, opportunities, and threats. Engineering 7 (9):1201–11. doi:10.1016/j.eng.2021.03.019.
  • Erdogan, A., and S. Canbazoglu. 2016. An investigation on energy consumption and 424 environmental effects of different architectural cases. Journal of Bartin University 425:51–60 .
  • Friedl, A., E. Padouvas, H. Rotter, and K. Varmuza. 2005. Prediction of heating values of biomass fuel from elemental composition. Analytica Chimica Acta 544 (1–2):191–98. doi:10.1016/j.aca.2005.01.041.
  • Golgiyaz, S., M. Daşkin, C. ONAT, and M. F. TALU. 2022. An artificial intelligence regression model for prediction of NOx emission from flame image. Journal of Soft Computing and Artificial Intelligence 3 (2):93–101. doi:10.55195/jscai.1213863.
  • Güleç, F., A. Samson, O. Williams, E. T. Kostas, and E. Lester. 2022. Biofuel characteristics of chars produced from rapeseed, whitewood, and seaweed via thermal conversion technologies – impacts of feedstocks and process conditions. Fuel Processing Technology 239:107492. doi:10.1016/j.fuproc.2022.107492.
  • Güleç, F., E. H. Şimşek, and H. Tanıker Sarı. 2022. Prediction of biomass pyrolysis mechanisms and kinetics: Application of the Kalman filter. Chemical Engineering & Technology 45 (1):167–177. doi:10.1002/ceat.202100229.
  • Huang, Y.-F., and S.-L. Lo. 2020. Predicting heating value of lignocellulosic biomass based on elemental analysis. Energy 191:116501. doi:10.1016/j.energy.2019.116501.
  • Jiménez, L., and F. González. 1991. Study of the physical and chemical properties of lignocellulosic residues with a view to the production of fuels. Fuel 70 (8):947–50. doi:10.1016/0016-2361(91)90049-G.
  • Kathiravale, S., M. N. M. Yunus, K. Sopian, A. H. Samsuddin, and R. A. Rahman. 2003. Modeling the heating value of municipal solid waste☆. Fuel 82(9):1119–25. doi:10.1016/S0016-2361(03)00009-7.
  • Li, X., Z. Huang, S. Shao, and Y. Cai. 2024. Machine learning prediction of physical properties and nitrogen content of porous carbon from agricultural wastes: Effects of activation and doping process. Fuel 356:129623. doi:10.1016/j.fuel.2023.129623.
  • Lyons, G. J., F. Lunny, and H. P. Pollock. 1985. A procedure for estimating the value of forest fuels. Biomass 8 (4):283–300. doi:10.1016/0144-4565(85)90061-7.
  • Majumder, A., R. Jain, P. Banerjee, and J. Barnwal. 2008. Development of a new proximate analysis based correlation to predict calorific value of coal. Fuel 87 (13–14):3077–81. doi:10.1016/j.fuel.2008.04.008.
  • Maksimuk, Y., Z. Antonava, V. Krouk, A. Korsakova, and V. Kursevich. 2021. Prediction of higher heating value (HHV) based on the structural composition for biomass. Fuel 299:1–7. doi:10.1016/j.fuel.2021.120860.
  • Ma, Y., W. Wang, H. Miao, S. Han, Y. Fu, Y. Chen, and J. Hao. 2024. Physicochemical synergistic effect of microwave-assisted Co-pyrolysis of biomass and waste plastics by thermal degradation, thermodynamics, numerical simulation, kinetics, and products analysis. Renewable Energy 223:120026. doi:10.1016/j.renene.2024.120026.
  • Nhuchhen, D. R., and M. T. Afzal. 2017. HHV predicting correlations for torrefied biomass using proximate and ultimate analyses. Bioengineering 4 (1):7. doi:10.3390/bioengineering4010007.
  • Nhuchhen, D. R., and P. A. Salam. 2012. Estimation of higher heating value of biomass from proximate analysis: A new approach. Fuel 99:55–63. doi:10.1016/j.fuel.2012.04.015.
  • Nunes, L. J., J. C. D. O. Matias, and J. P. D. S. Catalao. 2017. Torrefaction of biomass for energy applications: From fundamentals to industrial scale. London,United Kingdom: Academic Press.
  • Obernberger, I., T. Brunner, and G. Bärnthaler. 2006. Chemical properties of solid biofuels—significance and impact. Biomass and Bioenergy 30 (11):973–982. doi:10.1016/j.biombioe.2006.06.011.
  • Ozyuguran, A., A. Akturk, and S. Yaman. 2018. Optimal use of condensed parameters of ultimate analysis to predict the calorific value of biomass. Fuel 214:640–646. doi:10.1016/j.fuel.2017.10.082.
  • Özyuğuran, A., and S. Yaman. 2017. Prediction of calorific value of biomass from proximate analysis. Energy Procedia 107:130–136. doi:10.1016/j.egypro.2016.12.149.
  • Parikh, J., S. Channiwala, and G. Ghosal. 2005. A correlation for calculating HHV from proximate analysis of solid fuels. Fuel 84 (5):487–494. doi:10.1016/j.fuel.2004.10.010.
  • Qian, X., J. Xue, Y. Yang, and S. W. Lee. 2021. Thermal properties and combustion-related problems prediction of agricultural crop residues. Energies 14 (15):4619. doi:10.3390/en14154619.
  • Rahib, Y., B. Sarh, J. Chaoufi, S. Bonnamy, and A. Elorf. 2021. Physicochemical and thermal analysis of argan fruit residues (AFRs) as a new local biomass for bioenergy production. Journal of Thermal Analysis and Calorimetry 145 (5):2405–16. doi:10.1007/s10973-020-09804-7.
  • Saidur, R., E. A. Abdelaziz, A. Demirbas, M. S. Hossain, and S. Mekhilef. 2011. A review on biomass as a fuel for boilers. Renewable and Sustainable Energy Reviews 15 (5):2262–89. doi:10.1016/j.rser.2011.02.015.
  • Sheng, C., and J. Azevedo. 2005. Estimating the higher heating value of biomass fuels from basic analysis data. Biomass and Bioenergy 28 (5):499–507. doi:10.1016/j.biombioe.2004.11.008.
  • Telmo, C., J. Lousada, and N. Moreira. 2010. Proximate analysis, backwards stepwise regression between gross calorific value, ultimate and chemical analysis of wood. Bioresource Technology 101 (11):3808–15. doi:10.1016/j.biortech.2010.01.021.
  • Thipkhunthod, P., V. Meeyoo, P. Rangsunvigit, B. Kitiyanan, K. Siemanond, and T. Rirksomboon. 2005. Predicting the heating value of sewage sludges in Thailand from proximate and ultimate analyses. Fuel 84 (7–8):849–57. doi:10.1016/j.fuel.2005.01.003.
  • Vargas-Moreno, J., A. J. Callejón-Ferre, J. Pérez-Alonso, and B. Velázquez-Martí. 2012. A review of the mathematical models for predicting the heating value of biomass materials. Renewable and Sustainable Energy Reviews 16 (5):3065–83. doi:10.1016/j.rser.2012.02.054.
  • Wei, Z., Z. Cheng, and Y. Shen. 2024. Recent development in production of pellet fuels from biomass and polyethylene (PE) wastes. Fuel 358:130222. doi:10.1016/j.fuel.2023.130222.
  • Yin, C.-Y. 2011. Prediction of higher heating values of biomass from proximate and ultimate analyses. Fuel 90 (3):1128–32. doi:10.1016/j.fuel.2010.11.031.
  • Zanzi, R., K. Sjöström, and E. Björnbom. 2002. Rapid pyrolysis of agricultural residues at high temperature. Biomass and Bioenergy 23 (5):357–366. doi:10.1016/S0961-9534(02)00061-2.
  • Zhang, L., C. C. Xu, and P. Champagne. 2010. Overview of recent advances in thermo-chemical conversion of biomass. Energy Conversion and Management 51 (5):969–82. doi:10.1016/j.enconman.2009.11.038.