Predicting coal elemental components from proximate analysis: Explicit versus implicit nonlinear models

References

• Akkaya, A. V. 2009. Proximate analysis based multiple regression models for higher heating value estimation of low rank coals. Fuel Process Technology 90:165–70. doi:10.1016/j.fuproc.2008.08.016.
   Web of Science ®Google Scholar
• Akkaya, A. V. 2013. Predicting coal heating values using proximate analysis via a neural network approach. Energy Source Part A 35 (3):253–60. doi:10.1080/15567036.2010.509090.
   Google Scholar
• Akkaya, A. V. 2017 Random forest based estimation model for coal elemental components. International Conference on Energy and Thermal Engineering, Istanbul, Apr 25–28 504–08.
   Google Scholar
• Boulesteix, A. L., S. Janitza, and J. Kruppa. 2012. Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. WIREs Data Mining and Knowledge 129:1–31. doi:10.1002/widm.1072.
   Google Scholar
• Bragg LJ, Oman JK, Tewalt SJ, Oman CJ, Rega NH, Washington PM,  and Finkelman RB. 1998. U.S. geological survey coal quality (COALQUAL) database; version 2.0. doi:10.3133/ofr97134
   Google Scholar
• Breiman, L. 2001. Random forests. Machine Learning 45:5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Chakma, S., R. C. Vaishya, and A. K. Yadav. 2016. Modeling chemical compositions of municipal solid waste. Environmental Geotechnics 3:65–77. doi:10.1680/envgeo.13.00082.
   Web of Science ®Google Scholar
• Ghugare, S. B., S. Tiwary, and S. S. Tambe. 2017. Computational intelligence based models for prediction of elemental composition of solid biomass fuels from proximate analysis. International Journal Of Systems Assurance Engineering And Management 8:2083–96. doi:10.1007/s13198-014-0324-4.
   Web of Science ®Google Scholar
• Goudarzi, N., D. Shahsavani, F. Emadi-Gandaghi, and C. M. Arab. 2014. Application of random forests method to predict the retention indices of some polycyclic aromatic hydrocarbons. Journal of Chromatography. A 1333:25–31. doi:10.1016/j.chroma.2014.01.048.
   PubMed Web of Science ®Google Scholar
• Khandelwal, M., and T. N. Singh. 2010. Prediction of macerals contents of Indian coals from proximate and ultimate analyses using artificial neural networks. Fuel 89 (5):1101–09. doi:10.1016/j.fuel.2009.11.028.
   Web of Science ®Google Scholar
• Klasson, T. K. 2017. Biochar characterization and a method for estimating biochar quality from proximate analysis results. Biomass and Bioenergy 96:50–58. doi:10.1016/j.biombioe.2016.10.011.
   Web of Science ®Google Scholar
• Li, H., S. Yang, W. Zhao, Z. Xu, S. Zhao, and X. Liu. 2016. Prediction of the physicochemical properties of woody biomass using linear prediction and artificial neural networks. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 38 (11):1569–73. doi:10.1080/15567036.2014.934412.
   Web of Science ®Google Scholar
• MATLAB. The language of technical computing. (R2014b). Natick, MA: The Math Works Inc.
   Google Scholar
• Miraboutalebi, S. M. R., P. Kazemi, and P. Bahrami. 2016. Fatty Acid Methyl Ester (FAME) composition used for estimation of biodiesel cetane number employing random forest and artificial neural networks: A new approach. Fuel 166:143–51. doi:10.1016/j.fuel.2015.10.118.
   Web of Science ®Google Scholar
• Nhuchhen, D. R. 2016. Prediction of carbon, hydrogen, and oxygen compositions of raw and torrefied biomass using proximate analysis. Fuel 180:348–56. doi:10.1016/j.fuel.2016.04.058.
   Web of Science ®Google Scholar
• Parikh, J., S. Channiwala, and G. Ghosal. 2007. A correlation for calculating elemental composition from proximate analysis of biomass materials. Fuel 86:1710–19. doi:10.1016/j.fuel.2006.12.029.
   Web of Science ®Google Scholar
• Patel SU, Jeevan KB, Yogesh PB, Sharma BK, Saha S, Biswas S, Chaudhury A, Tambe SS, and Kulkarni BD. 2007. Estimation of gross calorific value of coals using artificial neural networks. Fuel 86:334–44. doi:10.1016/j.fuel.2006.07.036.
   Web of Science ®Google Scholar
• Petrakova, A., M. Affenzeller, and G. Merkurjeva. 2015. Heterogeneous versus homogeneous machine learning ensembles. Information Technology and Management Science 18:135–40. doi:10.1515/itms-2015-0021.
   Google Scholar
• Rodriguez-Galiano, V. F., M. Sanchez-Castillo, J. Dash, P. M. Atkinson, and J. Ojeda-Zujar. 2016. Modelling interannual variation in the spring and autumn land surface phenology of the European forest. Biogeosciences 13:3305–17. doi:10.5194/bg-13-3305-2016.
   Web of Science ®Google Scholar
• Saptoro, A., H. M. Yao, M. O. Tadé, and H. B. Vuthaluru. 2008. Prediction of coal hydrogen content for combustion control in power utility using neural network approach. Chemometrics and Intelligent Laboratory Systems 94:149–59. doi:10.1016/j.chemolab.2008.07.007.
   Web of Science ®Google Scholar
• Shen, J., S. Zhu, X. Liu, H. Zhang, and J. Tan. 2010. The prediction of elemental composition of biomass based on proximate analysis. Energy Conversion and Management 51:983–87. doi:10.1016/j.enconman.2009.11.039.
   Web of Science ®Google Scholar
• Steinberg, D., M. Golovnya, and N. S. Cardell A brief overview to random forests Accessed February 12, 2019. http://nymetro.chapter.informs.org/prac_cor_pubs/RandomForest_SteinbergD.pdf.
   Google Scholar
• Tewalt SJ, Belkin HE, SanFilipo JR, Merrill MD, Palmer CA, Warwick PD, Karlsen AW, Finkelman RB, and Park AJ. 2010. Survey, U.S.G. chemical analyses in the world coal quality inventory, version 1. Report 2010-1196. Reston, Virginia.
   Google Scholar
• Vakkilainen, E. K. 2000. Estimation of elemental composition from proximate analysis of black liquor. Paperi Ja Puu 82:450–54.
   Google Scholar
• Yi, L., J. Feng, Y. H. Qin, and W. Y. Li. 2017. Prediction of elemental composition of coal using proximate analysis. Fuel 193:315–21. doi:10.1016/j.fuel.2016.12.044.
   Web of Science ®Google Scholar