English
Arabic
Dutch
French
German
Hindi
Italian
Russian
Spanish
Abstract :
The escalating global pursuit of sustainable energy solutions has led to the emergence of biomass-derived fuels, such as biobriquettes, as feasible substitutes for traditional fossil fuels. Kesambi leaves, which are abundant in Southeast Asia and boast a high calorific value, represent a promising prospect for the production of biobriquettes. In this investigation, a conclusive analytical method is employed to construct a predictive framework for estimating the Higher Heating Value (HHV) of torrefied kesambi leaf biobriquettes. By incorporating ash content (PS), volatile matter (BR), carbon (C), hydrogen (H), and oxygen (O) percentages, alongside experimental HHV data, through multiple linear regression and elemental composition data acquired from proximal analysis, the model aims to forecast HHV. The model’s modest positive Mean Bias Error (MBE) and satisfactory Root Mean Square Error (RMSE) suggest a good fit. The substantial R-squared value indicates the model’s capability to adeptly capture HHV variability. Ultimately, this approach grounded in fundamental principles contributes significantly to the sustainable exploitation of biomass resources by providing a pragmatic and effective technique for predicting HHV for kesambi leaf biobriquettes.
Keywords :
higher heating value, kesambi leaves, predictive model, Renewable Energy, sustainability., Ultimate AnalysisReferences :
1 E. Heaslip, G. J. Costello, and J. Lohan, “Assessing good-practice frameworks for the development of sustainable energy communities in Europe: Lessons from Denmark and Ireland,” Journal of Sustainable Development of Energy, Water and Environment Systems, vol. 4, no. 3, 2016, doi: 10.13044/j.sdewes.2016.04.0024.
2 J. J. Vidal-Amaro and C. Sheinbaum-Pardo, “A transition strategy from fossil fuels to renewable energy sources in the mexican electricity system,” Journal of Sustainable Development of Energy, Water and Environment Systems, vol. 6, no. 1, 2018, doi: 10.13044/j.sdewes.d5.0170.
3 R. K. Lukman and P. Virtič, “Developing energy concept maps – An innovative educational tool for energy planning,” Journal of Sustainable Development of Energy, Water and Environment Systems, vol. 6, no. 4, 2018, doi: 10.13044/j.sdewes.d6.0219.
4 I. W. K. Suryawan et al., “Acceptance of Waste to Energy Technology by Local Residents of Jakarta City, Indonesia to Achieve Sustainable Clean and Environmentally Friendly Energy,” Journal of Sustainable Development of Energy, Water and Environment Systems, vol. 11, no. 2, 2023, doi: 10.13044/j.sdewes.d11.0443.
5 M. M. Tun, D. Juchelkova, M. M. Win, A. M. Thu, and T. Puchor, “Biomass energy: An overview of biomass sources, energy potential, and management in Southeast Asian countries,” Resources, vol. 8, no. 2. 2019. doi: 10.3390/resources8020081.
6 J. Jang and S. Y. Woo, “Forest biomass characterization and exploitation,” in Reference Module in Earth Systems and Environmental Sciences, 2023. doi: 10.1016/b978-0-323-93940-9.00042-6.
7 J. J. S. Dethan, F. J. Haba Bunga, M. E. S. Ledo, and J. C. Abineno, “Characteristics of Residence Time of the Torrefaction Process on the Results of Pruning Kesambi Trees,” Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering), vol. 13, no. 1, p. 102, Feb. 2024, doi: 10.23960/jtep-l.v13i1.102-113.
8 D. A. da Silva, E. Eloy, B. O. Caron, and P. F. Trugilho, “Elemental Chemical Composition of Forest Biomass at Different Ages for Energy Purposes,” Floresta e Ambiente, vol. 26, no. 4, 2019, doi: 10.1590/2179-8087.020116.
9 S. Adhikari, H. Nam, and J. P. Chakraborty, “Conversion of solid wastes to fuels and chemicals through pyrolysis,” in Waste Biorefinery: Potential and Perspectives, 2018. doi: 10.1016/B978-0-444-63992-9.00008-2.
10 B. Kampman et al., “BUBE: Better Use of Biomass for Energy – Background Report to the Position Paper of IEA RETD and IEA Bioenergy,” IEA RETD and IEA Bioenergy, 2010.
11 “FEASIBILITY OF SMALL SCALE BIOMASS POWER PLANTS IN SRI LANKA P R E P A R E D B Y P R E P A R E D F O R 2 7 M A R C H 2 0 2 0.”
12 J. Parikh, S. A. Channiwala, and G. K. Ghosal, “A correlation for calculating elemental composition from proximate analysis of biomass materials,” Fuel, vol. 86, no. 12–13, 2007, doi: 10.1016/j.fuel.2006.12.029.
13 J. Dethan and H. Lalel, “Optimization of Particle Size of Torrefied Kesambi Leaf and Binder Ratio on the Quality of Biobriquettes,” Journal of Sustainable Development of Energy, Water and Environment Systems, vol. 12, no. 1, pp. 1–21, Mar. 2024, doi: 10.13044/j.sdewes.d12.0490.
14 D. R. Nhuchhen, “Prediction of carbon, hydrogen, and oxygen compositions of raw and torrefied biomass using proximate analysis,” Fuel, vol. 180, 2016, doi: 10.1016/j.fuel.2016.04.058.
15 U. A. Dodo, E. C. Ashigwuike, J. N. Emechebe, and S. I. Abba, “Prediction of energy content of biomass based on hybrid machine learning ensemble algorithm,” Energy Nexus, vol. 8, 2022, doi: 10.1016/j.nexus.2022.100157.
16 S. A. Abdollahi, S. F. Ranjbar, and D. Razeghi Jahromi, “Applying feature selection and machine learning techniques to estimate the biomass higher heating value,” Sci Rep, vol. 13, no. 1, 2023, doi: 10.1038/s41598-023-43496-x.
17 A. Dashti, A. S. Noushabadi, M. Raji, A. Razmi, S. Ceylan, and A. H. Mohammadi, “Estimation of biomass higher heating value (HHV) based on the proximate analysis: Smart modeling and correlation,” Fuel, vol. 257, 2019, doi: 10.1016/j.fuel.2019.115931.
18 A. S. Noushabadi, A. Dashti, F. Ahmadijokani, J. Hu, and A. H. Mohammadi, “Estimation of higher heating values (HHVs) of biomass fuels based on ultimate analysis using machine learning techniques and improved equation,” Renew Energy, vol. 179, 2021, doi: 10.1016/j.renene.2021.07.003.
19 J. Jo, D. G. Lee, J. Kim, B. H. Lee, and C. H. Jeon, “Improved ANN-Based Approach Using Relative Impact for the Prediction of Thermal Coal Elemental Composition Using Proximate Analysis,” ACS Omega, vol. 7, no. 34, 2022, doi: 10.1021/acsomega.2c02324.
20 R. A. Ibikunle, A. F. Lukman, I. F. Titiladunayo, E. A. Akeju, and S. O. Dahunsi, “Modeling and robust prediction of high heating values of municipal solid waste based on ultimate analysis,” Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 2020, doi: 10.1080/15567036.2020.1841343.
21 A. Friedl, E. Padouvas, H. Rotter, and K. Varmuza, “Prediction of heating values of biomass fuel from elemental composition,” in Analytica Chimica Acta, 2005. doi: 10.1016/j.aca.2005.01.041.
22 S. Hosseinpour, M. Aghbashlo, M. Tabatabaei, and M. Mehrpooya, “Estimation of biomass higher heating value (HHV) based on the proximate analysis by using iterative neural network-adapted partial least squares (INNPLS),” Energy, vol. 138, 2017, doi: 10.1016/j.energy.2017.07.075.
23 H. Yaka, M. A. Insel, O. Yucel, and H. Sadikoglu, “A comparison of machine learning algorithms for estimation of higher heating values of biomass and fossil fuels from ultimate analysis,” Fuel, vol. 320, 2022, doi: 10.1016/j.fuel.2022.123971.
