When designing many variables influence the thermal conversion system for homogeneous biomass and heterogeneous biomass. The factor that can have an important influence is the higher the calorific value (HHV). Many correlation models have been developed to estimate the HHV of homogeneous and heterogeneous biomass to reduce analysis costs. Unfortunately, the correlation model for predicting biomass HHV still has shortcomings when predicting biomass data from different thermal conversion processes and different types of biomass. In this study, four new correlations based on proximate and ultimate analysis of homogeneous biomass with the hydrothermal carbonization (HTC) method used for HHV prediction are presented. The multiple linear regression method is used to produce correlations from homogeneous biomass data then compare biomass models from open literature and compare correlation accuracy for data from the literature. It was found that the correlation obtained from proximate analysis (HHV =967.171 -8.684Ash -9.299VM -9.913FC) was more accurate than that obtained from proximate.

AlNouss, A., McKay, G., & Al-Ansari, T. (2020). Production of syngas via gasification using optimum blends of biomass. Journal of Cleaner Production, 242, 118499. https://doi.org/10.1016/j.jclepro.2019.118499

Chen, W.-H., Hsu, H.-J., Kumar, G., Budzianowski, W. M., & Ong, H. C. (2017). Predictions of biochar production and torrefaction performance from sugarcane bagasse using interpolation and regression analysis. Bioresource Technology, 246, 12–19. https://doi.org/https://doi.org/10.1016/j.biortech.2017.07.184

Ebeling, J. M., & Jenkins, B. M. (1985). Physical and Chemical Properties of Biomass Fuels. Transactions of the American Society of Agricultural Engineers, 28(3), 898–902. https://doi.org/10.13031/2013.32359

Elneel, R., Anwar, S., & Ariwahjoedi, B. (2013). Prediction of heating values of oil palm fronds from ultimate analysis. In Journal of Applied Sciences (Vol. 13, Issue 3, pp. 491–496). https://doi.org/10.3923/jas.2013.491.496

ErdoÄŸan, S. (2021). LHV and HHV prediction model using regression analysis with the help of bond energies for biodiesel. Fuel, 301, 121065. https://doi.org/https://doi.org/10.1016/j.fuel.2021.121065

Erol, M., & Ku, S. (2010). Calorific value estimation of biomass from their proximate analyses data. 35, 170–173. https://doi.org/10.1016/j.renene.2009.05.008

Friedl, A., Padouvas, E., Rotter, H., & Varmuza, K. (2005a). Prediction of heating values of biomass fuel from elemental composition. Analytica Chimica Acta, 544(1-2 SPEC. ISS.), 191–198. https://doi.org/10.1016/j.aca.2005.01.041

Friedl, A., Padouvas, E., Rotter, H., & Varmuza, K. (2005b). Prediction of heating values of biomass fuel from elemental composition. Analytica Chimica Acta, 544(1-2 SPEC. ISS.), 191–198. https://doi.org/10.1016/j.aca.2005.01.041

Gonzblez, F., & Cbrdoba, D. (1997). Study of the physical and chemical properties of lignocellulosic residues a view to the production of fuels. 70, 947–950.

Islam, M. A., Kabir, G., Asif, M., & Hameed, B. H. (2015). Combustion kinetics of hydrochar produced from hydrothermal carbonisation of Karanj (Pongamia pinnata) fruit hulls via thermogravimetric analysis. Bioresource Technology, 194, 14–20. https://doi.org/10.1016/j.biortech.2015.06.094

Jimenez, L., & Gonzalez, F. (1991). Study of the physical and chemical properties of lignocellulosic residues with a view to the production of fuels. Fuel, 70, 947–950.

Kang, S., Li, X., Fan, J., & Chang, J. (2012). Solid fuel production by hydrothermal carbonization of black liquor. Bioresource Technology, 110, 715–718. https://doi.org/10.1016/j.biortech.2012.01.093

Nhuchhen, D. R., & Abdul Salam, P. (2012). Estimation of higher heating value of biomass from proximate analysis: A new approach. Fuel, 99, 55–63. https://doi.org/10.1016/j.fuel.2012.04.015

ÖzyuǧUran, A., & Yaman, S. (2017). Prediction of Calorific Value of Biomass from Proximate Analysis. Energy Procedia, 107(September 2016), 130–136. https://doi.org/10.1016/j.egypro.2016.12.149

Putra, H. E., Permana, D., & Djaenudin. (2022). Prediction of higher heating value of solid fuel produced by hydrothermal carbonization of empty fruit bunch and various biomass feedstock. Journal of Material Cycles and Waste Management, 24(6), 2162–2171. https://doi.org/10.1007/s10163-022-01463-0

Qian, H., Guo, X., Fan, S., Hagos, K., Lu, X., Liu, C., & Huang, D. (2016). A Simple Prediction Model for Higher Heat Value of Biomass. Journal of Chemical and Engineering Data, 61(12), 4039–4045. https://doi.org/10.1021/acs.jced.6b00537

Rajput, R. K. (1996). Engineering Thermodinamika Third Edition. In USA WB Sanders Company. Laxmi Publications (P) LTD. http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Hird+dition#3%5Cnhttp://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:T+hird+edition#1

Shaaban, A., Se, S. M., Dimin, M. F., Juoi, J. M., Mohd Husin, M. H., & Mitan, N. M. M. (2014). Influence of heating temperature and holding time on biochars derived from rubber wood sawdust via slow pyrolysis. Journal of Analytical and Applied Pyrolysis, 107, 31–39. https://doi.org/10.1016/j.jaap.2014.01.021

Sobek, S., & Werle, S. (2021). Solar pyrolysis of waste biomass: A comparative study of products distribution, in situ heating behavior, and application of model-free kinetic predictions. Fuel, 292(October 2020), 120365. https://doi.org/10.1016/j.fuel.2021.120365

Xiaorui, L., Jiamin, Y., & Longji, Y. (2023). Predicting the high heating value and nitrogen content of torrefied biomass using a support vector machine optimized by a sparrow search algorithm. RSC Advances, 13(2), 802–807. https://doi.org/10.1039/d2ra06869a

Yin, C.-Y. (2011). Prediction of higher heating values of biomass from proximate and ultimate analyses. Fuel, 90(3), 1128–1132. https://doi.org/https://doi.org/10.1016/j.fuel.2010.11.031