Modeling of Agricultural Waste Higher Heating Value Based on Proximate Analysis

Abstract:

A model of the higher heating value on a dry basis from the proximity analysis of agricultural wastes in Benin has been proposed in this article. This model was developed using agricultural residues such as shea shells and cakes, cotton and soybean stalks, corn cobs and peanut shells identified as part of the implementation of an experimental system. The validity of this model has been established for the Higher Heating Value (HHV) between 18.07 MJ/kg to 25.91 MJ/kg, Volatile Matter rate (%VM) 66.8% to 79.87%, Fixed Carbon rate 13.83% to 29.59%, and Ash content (%Ash) 3.47% to 6.3%. The model has an average absolute error of 2.79% and a bias error of 0.034%, significantly better than the most accurate literature prediction model, which offers a mean absolute error of 5.97% and –4.66% for the bias error. This work presents as well the first data from the proximity analysis of agricultural residues in Benin. These analyzes are carried out using a well-structured methodology that respects the standards and measures of simple random sampling forsample collection. Samples prepared under appropriate conditions are analyzed using standardized protocols for the agricultural wastes studied.

• Periodical: Advanced Engineering Forum (Volume 48)
• Pages: 95-106
• DOI: https://doi.org/10.4028/p-01a23l
• Online since: January 2023
• Authors: Alexandre Houemenou*, Patrice Chetangny, David Adamon, Victor Zogbochi, Sossou Houndedako, Latif Adéniyi Fagbemi, Gerald Barbier, Vianou Antoine, Didier Chamagne
• Keywords: Agricultural Wastes, Higher Heating Value, Prediction Model, Proximity Analysis

References

[1] F. Cucchiella, I. D'Adamo, and M. Gastaldi, Sustainable waste management: Waste to energy plant as an alternative to landfill,, Energy Convers. Manag., vol. 131, p.18–31, 2017,.

DOI: 10.1016/j.enconman.2016.11.012

[2] H. Shi, N. Mahinpey, A. Aqsha, and R. Silbermann, Characterization, thermochemical conversion studies, and heating value modeling of municipal solid waste,, Waste Manag., vol. 48, p.34–47, 2016,.

DOI: 10.1016/j.wasman.2015.09.036

[3] C. Sheng and J. L. T. Azevedo, Estimating the higher heating value of biomass fuels from basic analysis data,, Biomass and Bioenergy, vol. 28, no. 5, p.499–507, 2005,.

DOI: 10.1016/j.biombioe.2004.11.008

[4] C.-Y. Yin, Prediction of higher heating values of biomass from proximate and ultimate analyses Chun-Yang,, Fuel, vol. 90, no. 3, p.1128–1132, 2011,.

DOI: 10.1016/j.fuel.2010.11.031

[5] D. R. Nhuchhen and P. Abdul Salam, Estimation of higher heating value of biomass from proximate analysis: A new approach,, Fuel, vol. 99, p.55–63, 2012,.

DOI: 10.1016/j.fuel.2012.04.015

[6] T. Cordero, F. Marquez, J. Rodriguez-Mirasol, and J. Rodriguez, Predicting heating values of lignocellulosics and carbonaceous materials from proximate analysis,, Fuel, vol. 80, no. 11, p.1567–1571, 2001,.

DOI: 10.1016/s0016-2361(01)00034-5

[7] A. K. Majumder, R. Jain, P. Banerjee, and J. P. Barnwal, Development of a new proximate analysis based correlation to predict calorific value of coal,, Fuel, vol. 87, no. 13–14, p.3077–3081, 2008,.

DOI: 10.1016/j.fuel.2008.04.008

[8] A. ÖzyuǧUran and S. Yaman, Prediction of Calorific Value of Biomass from Proximate Analysis,, Energy Procedia, vol. 107, no. September 2016, p.130–136, 2017,.

DOI: 10.1016/j.egypro.2016.12.149

[9] C. Huang, L. Han, X. Liu, and Z. Yang, Models predicting calorific value of straw from the ash content,, Int. J. Green Energy, vol. 5, no. 6, p.533–539, 2008,.

DOI: 10.1080/15435070802498507

[10] J. M. Vargas-Moreno, A. J. Callejón-Ferre, J. Pérez-Alonso, and B. Velázquez-Martí, A review of the mathematical models for predicting the heating value of biomass materials,, Renew. Sustain. Energy Rev., vol. 16, no. 5, p.3065–3083, 2012,.

DOI: 10.1016/j.rser.2012.02.054

[11] S. Küçükbayrak, B. Dürüs, A. E. Meríçboyu, and E. Kadiog̈lu, Estimation of calorific values of Turkish lignites,, Fuel, vol. 70, no. 8, p.979–981, 1991,.

DOI: 10.1016/0016-2361(91)90054-e

[12] A. Demirbas, Prediction of higher heating values for vegetable oils and animal fats from proximate analysis data,, Energy Sources, Part A Recover. Util. Environ. Eff., vol. 31, no. 14, p.1264–1270, 2009,.

DOI: 10.1080/15567030802089532

[13] J. Parikh, S. A. Channiwala, and G. K. Ghosal, A correlation for calculating HHV from proximate analysis of solid fuels,, Fuel, vol. 84, no. 5, p.487–494, 2005,.

DOI: 10.1016/j.fuel.2004.10.010

[14] D. R. Nhuchhen and M. T. Afzal, HHV predicting correlations for torrefied biomass using proximate and ultimate analyses,, Bioengineering, vol. 4, no. 1, 2017,.

DOI: 10.3390/bioengineering4010007

[15] S. A. Channiwala and P. P. Parikh, A unified correlation for estimating HHV of solid, liquid and gaseous fuels,, Fuel, vol. 81, no. 8, p.1051–1063, 2002,.

DOI: 10.1016/s0016-2361(01)00131-4

[16] C. Qian, Q. Li, Z. Zhang, X. Wang, J. Hu, and W. Cao, Prediction of higher heating values of biochar from proximate and ultimate analysis,, Fuel, vol. 265, no. December 2019, p.116925, 2020,.

DOI: 10.1016/j.fuel.2019.116925

[17] W. S. Mcculloch and W. Pitts, A logical calculus of the ideas immanent in nervous activity,, Bulletin of Mathematical Biology, vol. 52, no. No. 1/2, p.99–115, (1990).

DOI: 10.1016/s0092-8240(05)80006-0

[18] 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, no. July, p.115931, 2019,.

DOI: 10.1016/j.fuel.2019.115931

[19] V. N. Vapnik, An Overview of Statistical Learning Theory,, vol. 10, no. 5, p.988–999, (1999).

[20] V. N. Vapnik, Statistical Learning Theory 1998, Springer science & business media, , vol. 2, (1998).

[21] T.-N. Do and P. François, Classification de grands ensembles de données avec un nouvel algorithme de SVM.,, In EGC, pp.739-750, (2007).

[22] J. S. R. Jang, Input selection for ANFIS learning,, IEEE Int. Conf. Fuzzy Syst., vol. 2, p.1493–1499, 1996,.

[23] A. Dashti, M. Raji, A. Azarafza, A. Baghban, A. H. Mohammadi, and M. Asghari, Rigorous prognostication and modeling of gas adsorption on activated carbon and Zeolite-5A,, J. Environ. Manage., vol. 224, no. May, p.58–68, 2018,.

DOI: 10.1016/j.jenvman.2018.06.091

[24] I. Boumanchar et al., Municipal solid waste higher heating value prediction from ultimate analysis using multiple regression and genetic programming techniques,, Waste Manag. Res., vol. 37, no. 6, p.578–589, 2019,.

DOI: 10.1177/0734242x18816797

[25] R. Singh, R. K. Umrao, M. Ahmad, M. K. Ansari, L. K. Sharma, and T. N. Singh, Prediction of geomechanical parameters using soft computing and multiple regression approach,, Meas. J. Int. Meas. Confed., vol. 99, p.108–119, 2017,.

DOI: 10.1016/j.measurement.2016.12.023

[26] X. Qian, S. Lee, A. M. Soto, and G. Chen, Regression model to predict the higher heating value of poultry waste from proximate analysis,, Resources, vol. 7, no. 3, 2018,.

DOI: 10.3390/resources7030039

[27] B. M. Jenkins, L. L. Baxter, T. R. Miles, and T. R. Miles, Combustion properties of biomass,, Fuel Process. Technol., vol. 54, no. 1–3, p.17–46, 1998,.

DOI: 10.1016/s0378-3820(97)00059-3

[28] Q. Feng, J. Zhang, X. Zhang, and S. Wen, Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network,, Fuel Process. Technol., vol. 129, p.120–129, 2015,.

DOI: 10.1016/j.fuproc.2014.09.001

[29] B. Fortunato, G. Brunetti, S. M. Camporeale, M. Torresi, and F. Fornarelli, Thermodynamic model of a downdraft gasifier,, Energy Convers. Manag., vol. 140, p.281–294, 2017,.

DOI: 10.1016/j.enconman.2017.02.061

[30] E. S. Hasan, M. Jahiding, Mashuni, W. O. S. Ilmawati, W. Wati, and I. N. Sudiana, Proximate and the Calorific Value Analysis of Brown Coal for High-Calorie Hybrid Briquette Application,, J. Phys. Conf. Ser., vol. 846, no. 1, 2017,.

DOI: 10.1088/1742-6596/846/1/012022

[31] K. Nattapong, W. Pilada, and J. Anuwat, Charcoal Briquettes from Madan Wood Waste as an Alternative Energy in Thailand,, Procedia Manuf., vol. 30, p.128–135, 2019,.

DOI: 10.1016/j.promfg.2019.02.019

[32] A. Tamilvanan, Preparation of Biomass Briquettes using Various Agro- Residues and Waste Papers,, J. Biofuels, vol. 4, no. 2, p.47, 2013,.

DOI: 10.5958/j.0976-4763.4.2.006