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Research Article

Statistical studies on high ash Indian coal crushed to (−3 mm) using 76 mm dense medium cyclone

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Pages 389-409 | Received 05 Nov 2018, Accepted 08 Apr 2019, Published online: 29 Apr 2019

References

  • Acharya, C., S. Mohanty, L. B. Sukla, and V. N. Misra. 2006. Prediction of sulphur removal with Acidithiobacillus sp. using artificial neural networks. Ecological Modelling 190 (1–2):223–30. doi:10.1016/j.ecolmodel.2005.02.021.
  • Al-Thyabat, S. 2008. On the optimization of froth flotation by the use of an artificial neural network. Journal of China University of Mining and Technology 18 (3):418–26. doi:10.1016/S1006-1266(08)60087-5.
  • Al-Thyabat, S. 2009. Investigating the effect of some operating parameters on phosphate flotation kinetics by neural network. Advanced Powder Technology 20 (4):355–60. doi:10.1016/j.apt.2009.01.004.
  • Chaurasia, R. C., D. Sahu, and S. Nikkam. 2018. Cleaning of coal by multi gravity separator. Transactions of the Indian Institute of Metals 71 (6):1487–95. doi:10.1007/s12666-018-1284-1.
  • Chaurasia, R. C., D. Sahu, and S. Nikkam. 2019. Prediction of ash content and yield percent of clean coal in multi gravity separator using artificial neural networks. International Journal of Coal Preparation and Utilization. doi:10.1080/19392699.2018.1547282.
  • Chaurasia, R. C., and S. Nikkam. 2016. Prediction of yield and combustible recovery of coal using Artificial Neural Network in MGS. Mineral Processing Technology International Conference-2016 (MPT-2016), 5–7 January 2016, Pune, Maharashtra.
  • Chaurasia, R. C., and S. Nikkam. 2017a. Application of artificial neural network to study the performance of multi-gravity separator (MGS) treating iron ore fines. Particulate Science and Technology 35 (1):93–102. doi:10.1080/02726351.2015.1131791.
  • Chaurasia, R. C., and S. Nikkam. 2017b. Beneficiation of low-grade iron ore fines by multi-gravity separator (MGS) using optimization studies. Particulate Science and Technology 35 (1):45–53. doi:10.1080/02726351.2015.1124161.
  • Chaurasia, R. C., and S. Nikkam. 2017c. Optimization studies on a multi-gravity separator treating ultrafine coal. International Journal of Coal Preparation and Utilization 37 (4):195–212. doi:10.1080/19392699.2016.1149474.
  • Chelgani, S. C., C. H. James, E. Jorjani, S. H. Mesroghli, and A. H. Bagherieh. 2008. Prediction of coal grindability based on petrography, proximate and ultimate analysis using multiple regression and artificial neural network models. Fuel Processing Technology 89 (1):13–20. doi:10.1016/j.fuproc.2007.06.004.
  • Chen, J., K. Chu, R. Zou, A. B. Yu, A. Vince, G. D. Barnett, and P. J. Barnett. 2017. Systematic study of the effect of particle density distribution on the flow and performance of a dense medium cyclone. Powder Technology 314:510–23. doi:10.1016/j.powtec.2016.11.041.
  • Chu, K. W., J. Chen, B. Wang, A. B. Yu, A. Vince, G. D. Barnett, and P. J. Barnett. 2017. Understand solids loading effects in a dense medium cyclone: Effect of particle size by a CFD-DEM method. Powder Technology 320:594–609. doi:10.1016/j.powtec.2017.07.032.
  • Cilek, E. C. 2002. Application of neural networks to predict locked cycle flotation test results. Minerals Engineering 15 (12):1095–104. doi:10.1016/S0892-6875(02)00259-5.
  • Clarkson, C. J. 1989. A model of dense medium cyclones. Coal Preparation 7 (3–4):159–74. doi:10.1080/15455838909407963.
  • Das, S. K., and N. Sivakugan. 2010. Discussion of intelligent computing for modeling axial capacity of pile foundations. Canadian Geotechnical Journal 37:928–30. doi:10.1139/T10-048.
  • Das, S. K., and P. K. Basudhar. 2006. Undrained lateral load capacity of piles in clay using artificial neural network. Computers and Geotechnics 33:454–59. doi:10.1016/j.compgeo.2006.08.006.
  • Das, S. K., and P. K. Basudhar. 2008. Prediction of residual friction angle of clays using artificial neural network. Engineering Geology 100:142–45. doi:10.1016/j.enggeo.2008.03.001.
  • De Korte, G. J. 2002. Dense-medium beneficiation of fine coal revisited. Journal of the Southern African Institute of Mining and Metallurgy 102 (7):393–96.
  • De Korte, G. J., and J. Engelbrecht. 2014. Dense medium cyclones. International Journal of Coal Preparation and Utilization 34 (1):49–58. doi:10.1080/19392699.2013.845009.
  • Garson, D. G. 1991. Interpreting neural network connection weights. Artificial Intelligence Expert 6 (7):47–51.
  • Goh, A. T. C. 1994. Seismic liquefaction potential assessed by neural networks. Journal of Geotechnical Engineering 120, No 9:1467–80. doi:10.1061/(ASCE)0733-9410(1994)120:9(1467).
  • Goh, A. T. C., F. H. Kulhawy, and C. G. Chua. 2005. Bayesian neural network analysis of undrained side resistance of drilled shafts. Journal of Geotechnical and Geoenvironmental Engineering 131 (1):84–93. doi:10.1061/(ASCE)1090-0241(2005)131:1(84).
  • Guyon, I., and A. Elisseeff. 2003. An introduction to variable and feature selection. Journal of Machine Learning Research 3:1157–82.
  • Honaker, R., R. Hollis, D. Switzer, and T. Coker. 2010. Development and evaluation of the CAVEX dense medium cyclone. International Journal of Coal Preparation and Utilization 30 (2–5):100–12. doi:10.1080/19392699.2010.497086.
  • Honaker, R. Q., F. Boaten, and G. H. Luttrell. 2007. Ultrafine coal classification using 150 mm gMax cyclone circuits. Minerals Engineering 20 (13):1218–26. doi:10.1016/j.mineng.2007.06.004.
  • Jorjani, E., C. S. Chehreh, and S. H. Mesroghli. 2007. Prediction of microbial desulfurization of coal using artificial neural networks. Minerals Engineering 20 (14):1285–92. doi:10.1016/j.mineng.2007.07.003.
  • Jorjani, E., C. S. Chehreh, and S. H. Mesroghli. 2008. Application of artificial neural networks to predict chemical desulfurization of Tabas coal. Fuel 87 (12):2727–34. doi:10.1016/j.fuel.2008.01.029.
  • Jorjani, E., H. Asadollahi Poorali, A. Sam, S. C. Chelgani, S. H. Mesroghli, and M. R. Shayestehfar. 2009. Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network. Minerals Engineering 22 (11):970–76. doi:10.1016/j.mineng.2009.03.003.
  • Jorjani, E., S. H. Mesroghli, and C. S. Chehreh. 2008. Prediction of operational parameters effect on coal flotation using artificial neural network. Journal of University of Science and Technology Beijing, Mineral, Metallurgy, Material 15 (5):528–33. doi:10.1016/S1005-8850(08)60099-7.
  • Kalyani, V. K., Pallavika, S. Chaudhuri, T. Gouri Charan, D. D. Haldar, K. P. Kamal, Y. P. Badhe, S. S. Tambe, and B. D. Kulkarni. 2007. Study of a laboratory-scale froth flotation process using artificial neural networks. Mineral Processing and Extractive Metallurgy Review 29 (2):130–42. doi:10.1080/08827500701421912.
  • Kalyani, V. K., T. Gouri Charan, D. D. Haldar, A. Sinha, and S. Nikkam. 2008. Coal-fine beneficiation studies of a bench-scale water-only cyclone using artificial neural network”. International Journal of Coal Preparation and Utilization 28 (2):94–114. doi:10.1080/19392690802069918.
  • Karimi, M., A. Dehghani, A. Nezamalhosseini, and S. Talebi. 2010. Prediction of hydrocyclone performance using artificial neural networks. Journal of the Southern African Institute of Mining and Metallurgy 110 (5):207–12.
  • Khoshjavan, S., B. Rezai, and M. Heidary. 2011. Evaluation of effect of coal chemical properties on coal swelling index using artificial neural networks. Expert Systems with Applications 38 (10):12906–12. doi:10.1016/j.eswa.2011.04.084.
  • Laberge, C., C. Daniel, and M. Guy. 2000. Metal bioleaching prediction in continuous processing of municipal sewage with Thiobacillus ferrooxidans using neural networks. Water Research 34 (4):1145–56. doi:10.1016/S0043-1354(99)00246-8.
  • Labidi, J., M. A. Pelach, X. Turon, and P. Mutje. 2007. Predicting flotation efficiency using neural networks. Chemical Engineering and Processing: Process Intensification 46 (4):314–22. doi:10.1016/j.cep.2006.06.011.
  • Liu, B., J. Sha, Z. Liu, G. Xie, and Y. Peng. 2018. Separation of 0.75–0.125 mm fine coal using the cylindrical section of a 710/500 mm three-product dense medium cyclone. International Journal of Coal Preparation and Utilization.38 (1):1–2. doi:10.1080/19392699.2015.1088528.
  • Magwai, M. K., and J. Bosman. 2008. The effect of cyclone geometry and operating conditions on spigot capacity of dense medium cyclones. International Journal of Mineral Processing 86 (1–4):94–103. doi:10.1016/j.minpro.2007.11.005.
  • Massinaei, M., and R. Doostmohammadi. 2010. Modeling of bubble surface area flux in an industrial rougher column using artificial neural network and statistical techniques. Minerals Engineering 23 (2):83–90. doi:10.1016/j.mineng.2009.10.005.
  • Math Work Inc. 2013. Matlab User’s Manual. Version 8. 1. 0. 604. (R2013a.) Natick (MA).
  • Mohanty, M. K., A. Palit, and B. Dube. 2002. A comparative evaluation of new fine particle size separation technologies. Minerals Engineering 15 (10):727–36. doi:10.1016/S0892-6875(02)00169-3.
  • Mohanty, S. 2009. Artificial neural network based system identification and model predictive control of a flotation column. Journal of Process Control 19 (6):991–99. doi:10.1016/j.jprocont.2009.01.001.
  • Nash, J. E., and J. V. Sutcliffe. 1970. River flow forecasting through conceptual models. Part: A Discussion of Principles. Journal of Hydrology. 10:282–90. doi:10.1016/0022-1694(70)90255-6.
  • Olden, J. D., M. K. Joy, and R. G. Death. 2004. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling 178 (3):389–97. doi:10.1016/j.ecolmodel.2004.03.013.
  • Ozbayoglu, G. A., M. Ozbayoglu, and M. E. Ozbayoglu. 2008. Estimation of Hardgrove grindability index of Turkish coals by neural networks. International Journal of Mineral Processing 85 (4):93–100. doi:10.1016/j.minpro.2007.08.003.
  • Panda, L., A. K. Sahoo, A. Tripathy, S. K. Biswal, and A. K. Sahu. 2012. Application of artificial neural network to study the performance of jig for beneficiation of non-coking coal. Fuel 97:151–56. doi:10.1016/j.fuel.2012.02.018.
  • Sahu, D., R. C. Chaurasia, and S. Nikkam. 2018. Mineralogical characterization and washability of indian coal from Jamadoba. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. doi:10.1080/15567036.2018.1520336.
  • Shahin, M. A., H. R. Maier, and M. B. Jaksa. 2002. Predicting settlement of shallow foundations using neural network. Journal of Geotechnical and Geoenvironmental Engineering, ASCE 128 (9):785–93. doi:10.1061/(ASCE)1090-0241(2002)128:9(785).
  • Sripriya, R., P. K. Banerjee, Soni, A. D. Baijal, A. Dutta, M. V. Rao, and S. Chatterjee. 2007. Dense-medium cyclone: Plant experience with high near-gravity material Indian coals. Coal Preparation 27 (1–3):78–106. doi:10.1080/07349340701249729.
  • Verghese, P. A., and T. C. Rao. 1994. Modelling of a 76 mm diameter dense medium cyclone. Coal Preparation 15 (1–2):71–91. doi:10.1080/07349349408905289.
  • Wilby, R. L., R. J. Abrahart, and C. W. Dawson. 2003. Detection of conceptual model rainfall-runoff processes inside an artificial neural network. Hydrological Sciences 48:163–81. doi:10.1623/hysj.48.2.163.44699.
  • Wills, B. A. 1992. Mineral Processing Technology. Oxford, UK: Pergamon Press.
  • Yadav, A. M., R. C. Chaurasia, N. Suresh, and P. Gajbhiye. 2018. Application of artificial neural networks and response surface methodology approaches for the prediction of oil agglomeration process. Fuel 220:826–36. doi:10.1016/j.fuel.2018.02.040.
  • Zeidenberg, M. 1990. Neural network models in artificial intelligence, 16. New York: Ellis Horwood.

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