http://orcid.org/0000-0002-6516-9603
References
- Akkaya, A.V. 2009. Proximate analysis based multiple regression models for higher heating value estimation of low rank coals. Fuel Processing Technology, 90(2), 165–170.
- B.P. (2015). Statistical Review of World Energy 2015. Retrieved from https://www.bp.com/content/dam/bp/pdf/energy-economics/statistical-review-2015/bp-statistical-review-of-world-energy-2015-full-report.pdf
- Chelgani, S.C., and Makaremi, S. 2013. Explaining the relationship between common coal analyses and Afghan coal parameters using statistical modeling methods. Fuel Processing Technology, 110, 79–85.
- Conag, A.T., Villahermosa, J.E.R, Cabatingan, L.K, Go A.W. 2018. Predictive HHV Model for Raw and Torrefied Sugarcane Residues. Waste and Biomass Valorization doi:10.1007/s12649-018-0204-2
- Feng, Q., Zhang, J., Zhang, X., and Wen, S. 2015. Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network. Fuel Processing Technology, 129, 120–129.
- Ghugare, S.B., and Tambe, S.S. 2016. Genetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies. Journal of the Energy Institute. doi:10.1016/j.joei.2016.03.002
- Kavšek, D., Bednárová, A., Biro, M., Kranvogl, R., Vončina, D.B., and Beinrohr, E. 2013. Characterization of Slovenian coal and estimation of coal heating value based on proximate analysis using regression and artificial neural networks. Central European Journal of Chemistry, 11(9), 1481–1491. doi:10.2478/s11532-013-0280-x
- Majumder, A., Jain, R., Banerjee, P., and Barnwal, J. 2008. Development of a new proximate analysis based correlation to predict calorific value of coal. Fuel, 87(13–14), 3077–3081.
- Mesroghli, S., Jorjani, E., and Chehreh Chelgani, S. 2009. Estimation of gross calorific value based on coal analysis using regression and artificial neural networks. International Journal of Coal Geology, 79(1–2), 49–54. doi:10.1016/j.coal.2009.04.002
- Palmer, C.A., Oman, C.L., Park, A.J., and Luppens, J.A. (2015). The US Geological Survey coal quality (COALQUAL) database version 3.0. Retrieved from http://ncrdspublic.er.usgs.gov/coalqual/
- Parikh, J., Channiwala, S.A., and Ghosal, G.K. 2007. A correlation for calculating elemental composition from proximate analysis of biomass materials. Fuel, 86(12–13), 1710–1719. doi:10.1016/j.fuel.2006.12.029
- Patel, S.U., Kumar, B.J., Badhe, Y.P., Sharma, B., Saha, S., Biswas, S., … Kulkarni, B.D. 2007. Estimation of gross calorific value of coals using artificial neural networks. Fuel, 86(3), 334–344.
- Tan, P., Zhang, C., Xia, J., Fang, Q.-Y., and Chen, G. 2015. Estimation of higher heating value of coal based on proximate analysis using support vector regression. Fuel Processing Technology, 138, 298–304.
- Tewalt, S.J., Belkin, H.E., SanFilipo, J.R., Merrill, M.D., Palmer, C.A., Warwick, P.D., … Park, A.J. (2010). Chemical analyses in the world coal quality inventory. Retrieved from http://pubs.usgs.gov/of/2010/1196/
- Vargas-Moreno, J., Callejón-Ferre, A., Pérez-Alonso, J., and Velázquez-Martí, B. 2012. A review of the mathematical models for predicting the heating value of biomass materials. Renewable and Sustainable Energy Reviews, 16(5), 3065–3083.
- Yin, C.-Y. 2011. Prediction of higher heating values of biomass from proximate and ultimate analyses. Fuel, 90(3), 1128–1132.