Volatile matter values change according to the standard utilized?
DOI:
https://doi.org/10.33448/rsd-v10i12.20476Keywords:
Proximate analysis; ASTM; ISO; Biomass.Abstract
In lignocellulosic biomass, the volatile matter can vary from 65 to 85%. Different standards are described in the literature for obtaining this parameter. However, it is observed that some studies of regression models have not considered these differences. They create a volatile matter content database, where the standards for obtaining the same parameter are different. Thus, the objective of the study was to verify whether different standards for volatile matter present statistically equal values. That is if they can be compared with each other, without danger of bias. For this, three types of biomasses of Brazil used were used (eucalyptus chips, pine chips, and sugarcane bagasse). The samples were collected, size reduction, size separation, and stored in the laboratory. Three standards of the American Society for Testing and Materials (ASTM D1762, ASTM E872, ASTM D3175) and one standard of the International Organization for Standardization (ISO 18123) were tested. The experimental design was completely randomized, consisting of four treatments and five replications. The central limit theorem was tested in some literature databases of the volatile matter. The results showed statistical differences when changing the type of standard used. For eucalyptus sawdust, the four standards resulted in methodologies with different averages. Still, the central limit theorem was not observed in some databases of different articles. This was explained by the non-standardization of a single standard when grouping data from different works. Therefore, different volatile content standards produce different results and when comparing values, it is important to take this assumption into account.
References
American Society of Testing Materials (2013). D1762: Standard Test Method for Chemical Analysis of Wood Charcoal. West Conshohocken.
American Society of Testing Materials (2011). D3175: Standard Test Method for Volatile Matter in the Analysis Sample of Coal and Coke. West Conshohocken.
American Society of Testing Materials (2013). E872: Standard Test Method for Volatile Matter in the Analysis of Particulate Wood Fuels. West Conshohocken.
Barbin, D. (2013). Planejamento e Análise Estatística de Experimentos Agronômicos. ESALQ.
Channiwala, S. A., & Parikh, P. P. (2020). A unified correlation for estimating HHV of solid, liquid and gaseous fuels. Fuel. 81 (8): 1051-63.
Debdoubi, A., El Amarti, A., & Colacio, E. (2005). Production of fuel briquettes from esparto partially pyrolyzed. Energy Conversion and Management. 46(11-12): 1877-84.
Demirbaş, A. (1997) Calculation of higher heating values of biomass fuels. Fuel. 76 (5): 431-34.
García, R., Pizarro, C., Lavín, A. G., & Bueno, J. L. (2012). Characterization of Spanish biomass wastes for energy use. Bioresource Technology. 103(1): 249-258.
Hansted, A. L. S., Nakashima, G. T., Martins, M. P., Yamamoto, H., & Yamaji, F. M. (2016). Comparative analyses of fast growing species in different moisture content for high quality solid fuel production. Fuel. 184(15): 180-84.
Hosseinpour, S., Aghbashlo, M., Tabatabaei, M., & Mehrpooya, M. (2017). Estimation of biomass higher heating value (HHV) based on the proximate analysis by using iterative neural network-adapted partial least squares (INNPLS). Energy. 138(1): 473-79.
International Organization for Standardization (2015). EN-ISO 18123: Solid biofuels: Determination of the content of volatile matter.
Jenkins, B. M., Baxter, L. L., Miles, J. R., & Miles, T. R. (1998). Combustion properties of biomass. Fuel Processing Technology. 54(1-3): 17-46.
Küçükbayrak, S., Dürüs, B., Meríçboyu, A. E., & Kadiog̈lu, E. (1991) Estimation of calorific values of Turkish lignites. Fuel. 70(8): 979-81.
Miranda, M. T., Arranz, J. I., Rojas, S., & Montero, I. (2009). Energetic characterization of densified residues from Pyrenean oak forest. Fuel. 88 (11) 2106-12.
Morettin, P. A. (2010). Estatística básica: probabilidade, inferência, volume único. 1ª ed. São Paulo: Pearson.
Nakashima, G. T., Adhmann, I. C. S., Hansted, A. L. S., Belini, G. B., Waldman, W. R., & Yamaji, F. M. (2017). Materiais Lignocelulósicos: Caracterização e Produção de Briquetes. Revista Virtual de Química. 9(1): 150 - 62.
Nimon, K. & Reio Jr, T. G. (2011). Measurement invariance: a foundational principle for quantitative theory building. Human Resource Development Review. 10(2): 198-14.
Nhuchhen, D. R. & Salam, P. A. (2012). Estimation of higher heating value of biomass from proximate analysis: A new approach. Fuel. 99: 55-63.
Nogueira, L. A. H., Lora, E. E. S., Trossero, M. A, & Frisk, T. (2000). Dendroenergia: Fundamentos e Aplicações. Brasília: AEEL,
Özyuğuran, A., & Yaman, S. (2017). Prediction of Calorific Value of Biomass from Proximate Analysis. Energy Procedia. 107: 130-36.
Obernberger, I., & Thek, G. (2004). Physical chatacterisation and chemical composition of densified biomass fuels with regard to their combustion behavior. Biomass and Bioenergy. 27(6): 653 - 69.
Parikh, J., Channiwala, S. A., & Ghosal, G. K. (2005). A correlation for calculating HHV from proximate analysis of solid fuels. Fuel. 84(5): 487-94.
Posom, J., Shrestha, A., Saechua, W., & Sirisomboon, P. (2016). Rapid non-destructive evaluation of moisture content and higher heating value of Leucaena leucocephala pellets using near infrared spectroscopy. Energy. 107(15): 464-72.
Quinn, G. P. & Keough, M. J. (2010). Experimental Design and Data Analysis for Biologists. Cambridge University Press.
Sheng, C., & Azevedo, J. L. T. (2005). Estimating the higher heating value of biomass fuels from basic analysis data. Biomass and Bioenergy. 28(5): 499-07.
Smith, A. K. G., Alesi, L. S., Varanda, L. D., Silva, D. A., Santos, L. O., & Yamaji, F. M (2019). Production and evaluation of briquetes from urban pruning residue and sugarcane bagasse. Rev. Bras. Eng. Agríc. Ambiental. 23(2): 138-43.
Suárez, J. A., Luengo, C. A., Felfli, F. F., Bezzon, G., & Beatón, P. A. (2000). Thermochemical Properties of Cuban Biomass. Energy Sources. 22(10): 851-57.
Thy, P., Esbensen, K. H., & Jenkins, B. M. (2009). On representative sampling and reliable chemical characterization in thermal biomass conversion studies. Biomass and Bioenergy. 33(11): 1513-19.
Thipkhunthod, P., Meeyoo, V., Rangsunvigit, P., Kitiyanan, B., Seimanond, K., & Rirksomboon, T. (2005). Predicting the heating value of sewage sludges in Thailand from proximate and ultimate analyses. Fuel. 84 (7-8): 849-57.
Tortosa Masiá, A. A., Buhre, B. J. P., Gupta, R. P., & Wall, T. F. (2007). Characterising ash of biomass and waste, Fuel Processing Technology. 88(11-12): 1071-81.
Uzun, H., Yildiz, Z., Goldfarb, J. L., & Ceylan, S. (2017) Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis. Bioresource Technology. 234: 122-30.
Vaux, D. L., Fidler, F., & Cumming, G. (2012). Replicates and repeats--what is the difference and is it significant? A brief discussion of statistics and experimental design. EMBO reports. 13: 291–96.
Yang, X., Wang, H., Strong, J., Xu, S., Liu, S., Lu, K., Sheng, K., Guo, J., Che, L., He, L., Ok, Y. S., Yuan, G., Shen, Y., & Chen, X. (2017). Thermal Properties of Biochars Derived from Waste Biomass Generated by Agricultural and Forestry Sectors. Energies. 10(4): 469-81.
Yin, C. Y. (2011). Prediction of higher heating values of biomass from proximate and ultimate analyses. Fuel. 90 (3): 1128-32.
Zhang, Z., Zhang, Y., Zhou, Y., Ahmad, R., Pemberton-Pigott, C., Annegarn, H., & Dong, R. (2017). Systematic and conceptual errors in standards and protocols for thermal performance of biomass stoves. Renewable and Sustainable Energy Reviews. 72: 1343-54.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Diego Aleixo da Silva; Ana Larissa Santiago Hansted; Gabriela Tami Nakashima; Elias Ricardo Durango Padilla; Julio Cesar Pereira; Fabio Minoru Yamaji
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
