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Tytuł artykułu

Comparative analysis of predictive models for Tamarindus indica waste briquettes higher heating value

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Utilising biomass waste as a renewable energy source has gained a lot of interest as a means of reducing reliance on fossil fuels. Among the many types of biomass, tamarind fruit peel (Tamarindus indica), which is commonly discarded, holds promise as a feedstock for briquette production due to its favorable combustion properties. In order to ascertain the higher heating value (HHV) of briquettes made from tamarind peel waste, this study employs proximate analysis, which takes into account the materials’ moisture content, ash content, volatile matter, and fixed carbon. In this research, three models Wahid, Nhuchhen and Afzal, and Kieseler were comparatively analyzed to predict the HHV of tamarind peel briquettes. The study also explored the effects of particle size and binder ratio on briquette performance, specifically on HHV and combustion properties. Tamarind peel was processed into different powder sizes, mixed with varying binder ratios, and formed into briquettes. The three predictive models were statistically evaluated using R2, the Bayesian information criterion (BIC), and the akaike information criterion (AIC) after the briquettes were proximally analysed. With an R2 of 0.96, the Wahid model showed the highest prediction accuracy, followed by Nhuchhen (0.93) and Kieseler (0.78), according to the data. Wahid’s model also had the lowest AIC (45.3) and BIC (47.1), indicating it is the most efficient model for predicting the HHV of tamarind peel briquettes. According to the study, the best combinations for improved briquette performance were determined when particle size and binder ratio were found to have a substantial impact on the combustion characteristics. By turning leftover tamarind peel into a renewable energy source, this study promotes environmentally friendly waste management while also fostering energy innovation. The findings provide valuable insights into the optimization of biomass briquette production and highlight the potential of tamarind peel as an underutilized biomass resource.
Rocznik
Strony
345--354
Opis fizyczny
Bibliogr. 29 poz., rys., tab.
Twórcy
  • Department of Agriculture Engineering, Artha Wacana Christian University, Adisucipto Street, Kupang, Indonesia
  • Department of Agriculture Engineering, Artha Wacana Christian University, Adisucipto Street, Kupang, Indonesia
  • Department of Biology, Artha Wacana Christian University, Adisucipto Street, Kupang, Indonesia
  • Department of Agricultural Product Technology, Artha Wacana Christian University, Adisucipto Street, Kupang, Indonesia
Bibliografia
  • 1. Abdul Wahid, F. R. A., Saleh, S., & Abdul Samad, N. A. F. (2017). Estimation of higher heating value of torrefied palm oil wastes from proximate analysis. Energy Procedia, 138. https://doi.org/10.1016/j.egypro.2017.10.102
  • 2. Bao, J., Chu, M., Wang, H., Liu, Z., Han, D., Cao, L., Guo, J., & Zhao, Z. (2020). Evolution characteristics and influence mechanism of binder addition on metallurgical properties of iron carbon agglomerates. metallurgical and materials transactions B: Process Metallurgy and Materials Processing Science, 51(6). https://doi.org/10.1007/s11663-020-01962-1
  • 3. Brewer, M. J., Butler, A., & Cooksley, S. L. (2016). The relative performance of AIC, AICC and BIC in the presence of unobserved heterogeneity. Methods in Ecology and Evolution, 7(6). https://doi.org/10.1111/2041-210X.12541
  • 4. de Melo Silva Cheloni, L. M., Guedes Cota, T., & Reis, É. L. (2024). Evaluation of compressive strength of manganese oxidized ore briquettes: effects of compaction pressure, curing time, and percentage of binder. Mineral Processing and Extractive Metallurgy Review. https://doi.org/10.1080/08 827508.2024.2306586
  • 5. Dethan, J. J. S., Bale-Therik, J. F., Telupere, F. M. S., Lalel, H. J. D., & Adisasmito, S. (2024a). Characteristics of Kesambi leaf torrefaction biomass. AIP Conference Proceedings, 3073(1). https://doi.org/10.1063/5.0193717
  • 6. Dethan, J. J. S., Haba Bunga, F. J., Ledo, M. E. S., & Abineno, J. C. (2024b). Characteristics of residence time of the torrefaction process on the results of pruning Kesambi trees. Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering), 13(1), 102. https://doi.org/10.23960/jtep-l.v13i1.102-113
  • 7. Dethan, J.J.S., & Lalel, H. (2024). 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, 12(1), 1–21. https://doi.org/10.13044/j.sdewes.d12.0490
  • 8. Dethan, J.J.S. (2024). Evaluation of an empirical model for predicting the calorific value of biomass briquettes from candlenut shell and kesambi twigs. Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering, 7(3), 253–264.
  • 9. Enriquez-Medina, I., Rodas-Ortiz, I., Bedoya-Garcia, I., Velasquez-Godoy, A. M., Alvarez-Vasco, C., & Ceballos Bermudez, A. (2024). Bridging gap between agro-industrial waste, biodiversity a nd mycelium-based biocomposites: Understanding their properties by multiscale methodology. Journal of Bioresources and Bioproducts, 9(4), 495–507. https://doi.org/10.1016/J.JOBAB.2024.07.001
  • 10. Hao, X., Liu, X., Zhang, Z., Zhang, W., Lu, Y., Wang, Y., & Yang, T. (2022). In-depth insight into the cementitious synergistic effect of steel slag and red mud on the properties of composite cementitious materials. Journal of Building Engineering, 52. https://doi.org/10.1016/j.jobe.2022.104449
  • 11. Harbecke, J., Grunau, J., & Samanek, P. (2024). Are the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC) applicable in determining the optimal fit and simplicity of mechanistic models? International Studies in the Philosophy of Science, 37(1–2). https://doi.org/10.1080/02.698595.2024.2304487
  • 12. Jansen, M. (2024). Information criteria for structured parameter selection in high-dimensional tree and graph models. Digital Signal Processing: A Review Journal, 148. https://doi.org/10.1016/j.dsp.2024.104437
  • 13. Kieseler, S., Neubauer, Y., & Zobel, N. (2013). Ultimate and proximate correlations for estimating the higher heating value of hydrothermal solids. Energy and Fuels, 27(2). https://doi.org/10.1021/ef301752d
  • 14. Kitagawa, G. (2023). Information Criterion for a Large Scale Subset Regression Models. https:// arxiv.org/abs/2309.08110v1
  • 15. Kuha, J. (2004). AIC and BIC: Comparisons of assumptions and performance. Sociological Methods and Research 33(2). https://doi.org/10.1177/0049124103262065
  • 16. Lubwama, M., Birungi, A., Nuwamanya, A., & Yiga, V. A. (2024). Characteristics of rice husk biochar briquettes with municipal solid waste cassava, sweet potato and matooke peelings as binders. Materials for Renewable and Sustainable Energy, 13(2), 243– 254. https://doi.org/10.1007/S40243-024-00262-X/ tables/3
  • 17. Lu, L., & Zhang, Z. (2022). How to select the best fit model among bayesian latent growth models for complex data. Journal of Behavioral Data Science, 2(1). https://doi.org/10.35566/jbds/v2n1/p2
  • 18. Manotham, S., & Tesavibul, P. (2022). Effect of particle size on mechanical properties of alumina ceramic processed by photosensitive binder jetting with powder spattering technique. Journal of the European Ceramic Society, 42(4). https://doi.org/10.1016/j.jeurceramsoc.2021.11.062
  • 19. Muela, S. B., & López-Martín, C. (2023). A Comparison of information criterion for choosing copula models. International Business Research, 16(4). https://doi.org/10.5539/ibr.v16n4p1
  • 20. Nhuchhen, D. R., & Afzal, M. T. (2017). HHV predicting correlations for torrefied biomass using proximate and ultimate analyses. Bioengineering, 4(1). https:// doi.org/10.3390/bioengineering4010007
  • 21. Rahman, K. M., Miyanaji, H., & Williams, C. B. (2023). Effects of binder droplet size and powder particle size on binder jetting part properties. Rapid Prototyping Journal, 29(8), 1715–1729. https://doi.org/10.1108/RPJ-10-2022-0358
  • 22. Roberts, J. W., Sutcliffe, C. J., Green, P. L., & Black, K. (2020). Modelling of metallic particle binders for increased part density in binder jet printed components. Additive Manufacturing, 34. https://doi.org/10.1016/j.addma.2020.101244
  • 23. Sen, S., & Bradshaw, L. (2017). Comparison of relative fit indices for diagnostic model selection. Applied Psychological Measurement, 41(6). https:// doi.org/10.1177/0146621617695521
  • 24. Sen, S., & Cohen, A. S. (2024). An evaluation of fit indices used in model selection of dichotomous mixture IRT models. Educational and Psychological Measurement, 84(3). https://doi.org/10.1177/00131644231180529
  • 25. Setter, C., Ataíde, C. H., Mendes, R. F., & de Oliveira, T. J. P. (2021). Influence of particle size on the physico-mechanical and energy properties of briquettes produced with coffee husks. Environmental Science and Pollution Research, 28(7), 8215–8223. https://doi.org/10.1007/S11356-020-11124-0/ METRICS
  • 26. Soliman, N., Omran, A., Aghaee, K., Ozbulut, O., & Tagnit-Hamou, A. (2024). Synergistic effect of nano-to-macro waste glass of various particle sizes on ultra-high-performance concrete: Tradeoff between mix design parameters and performance through a statistical design approach. Journal of Building Engineering, 95, 110129. https://doi.org/10.1016/J. JOBE.2024.110129
  • 27. Sun, J., Meng, L., Hou, W., Yang, H., Weng, X., & Xu, J. (2022). Research on reliability model selection of computer network equipment based on AIC. 2022 International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 225–228. https://doi.org/10.1109/ ICICML57342.2022.10009838
  • 28. Yang, Y. (2005). Can the strengths of AIC and BIC be shared? A conflict between model indentification and regression estimation. Biometrika, 92(4). https://doi.org/10.1093/biomet/92.4.937
  • 29. Zhang, T. tong, Lin, C., Li, J. hui, Li, Y. J., & Xu, S. ying. (2024). Fabricating coconut palm-based rigid polyurethane foam with enhanced compressive strength using biomass waste. Polymer, 310, 127472. https://doi.org/10.1016/J.POLYMER.2024.127472
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-467d0b3e-0129-4b66-bb79-208282a1bcb6
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