Application of Statistical and Machine Learning Techniques for Laboratory-Scale Pressure Filtration: Modeling and Analysis of Cake Moisture

References

• Alam, N., Ozdemir, O., Hampton, M., and Nguyen, A., 2011, “Dewatering of coal plant tailings: flocculation followed by filtration.” Fuel, 90. pp. 26–35. doi:10.1016/j.fuel.2010.08.006
   Web of Science ®Google Scholar
• Beck, M., 2015, NeuralNetTools: Visualization and Analysis Tools for Neural Networks, R Package.
   Google Scholar
• Foley, G., 2006, “A review of factors affecting filter cake properties in dead-end microfiltration of microbial suspensions.” Journal of Membrane Science, 274. pp. 38–46.
   Web of Science ®Google Scholar
• Fritsch, S., and Guenther, F., 2012, Neuralnet: Training of Neural Networks, R Package.
   Google Scholar
• Garson, G. D., 1991, “Interpreting neural network connection weights.” International Journal of Artificial Intelligence and Expert Systems, 6. pp. 47–51.
   Google Scholar
• Gevry, M., Dimopoulos, I., and Lek, S., 2003, “Review and comparison of methods to study the contribution of variables in artificial neural network models.” Ecological Modeling, 160. pp. 249–264.
   Web of Science ®Google Scholar
• Ghandehari, S., Montazer-Rahmati, M. M., and Asghari, M., 2013, “Modeling the flux decline during protein microfiltration: a comparison between feed-forward back propagation and radial basis function neural networks modeling the flux decline during protein microfiltration: A comparis.” Separation Science and Technology, 48(9). pp. 1324–1330. doi:10.1080/01496395.2012.736914
   Web of Science ®Google Scholar
• Green, M. D., Eberl, M., and Landman, K. A., 1996, “Compressive yield stress of flocculated suspensions: determination via experiment.” AIChE Journal, 42. pp. 2308–2318.
   Web of Science ®Google Scholar
• Groppo, J. G., Sung, D. J., and Parekh, B. K., 1995, “Evaluation of hyperbaric filtration for fine coal dewatering : part I.” Minerals and Metallurgical Processing, 12. pp. 28–33.
   Google Scholar
• Iritani, E., Katagiri, N., and Kanetake, S., 2012, “Determination of cake filtration characteristics of dilute suspension of bentonite from various filtration tests.” Separation and Purification Technology, 92. pp. 143–151.
   Web of Science ®Google Scholar
• Jorjani, E., Asadollahi Poorali, H., Sam, A., Chehreh Chelgani, S., Mesroghli, S., and Shayestehfar, M. R., 2009, “Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network.” Minerals Engineering, 22. pp. 970–976. doi:10.1016/j.mineng.2009.03.003
   Web of Science ®Google Scholar
• Karimi, M., Dehghani, A., Nezamalhosseini, A., and Talebi, S., 2010, “Prediction of hydrocyclone performance using artificial neural networks.” The Journal of the Southern African Institute of Mining and Metallurgy, 110. pp. 207–212.
   Web of Science ®Google Scholar
• Klima, M. S., Arnold, B. J., Munjack, J., and Barry, B., 2013, Application of a pilot-scale plate filter press in dewatering coal slurries, In Environmental Considerations in Energy Production, (J. R. Craynon, Ed.), Littleton, CO: Society for Mining, Metallurgy, and Exploration Inc, pp. 42–50.
   Google Scholar
• Klima, M. S., Raman, G. S. S., Ganter, J., Smith, J., Hellgren, L., and Pollack, J., 2015, Evaluation of bench-scale pressure filtration when dewatering coal refuse slurry, In Second Environmental Considerations in Energy Production Conference, (J. Craynon, Ed.), Littleton, CO: Society for Mining, Metallurgy, and Exploration Inc, pp. 1–11.
   Google Scholar
• Kumar, S., Bhattacharya, S., and Mandre, N. R., 2014, “Characterization and flocculation studies of fine coal tailings.” The Journal of the Southern African Institute of Mining and Metallurgy, 114. pp. 945–949.
   Web of Science ®Google Scholar
• Landman, K., White, L. R., and Eberl, M., 1995, “Pressure filtration of flocculated suspensions.” AIChE Journal, 41. pp. 1687–1700.
   Web of Science ®Google Scholar
• Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J., and Aulagnier, S., 1996, “Application of neural networks to modelling nonlinear relationships in ecology.” Ecological Modeling, 90. pp. 39–52.
   Web of Science ®Google Scholar
• Mashhadi Meighani, H., Dehghani, A., Rekabdar, F., Hemmati, M., and Goodarznia, I., 2013, “Artificial intelligence vs. classical approaches: A new look at the prediction of flux decline in wastewater treatment.” Desalination and Water Treatment, 51. pp. 7476–7489. doi:10.1080/19443994.2013.773861
   Web of Science ®Google Scholar
• Olden, J. D., Joy, M. K., and Death, R. G., 2004, “An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data.” Ecological Modelingo, 178. pp. 389–397. doi:10.1016/j.ecolmodel.2004.03.013
   Web of Science ®Google Scholar
• Panda, L., Sahoo, A. K., Tripathy, A., Biswal, S. K., and Sahu, A. K., 2012, “Application of artificial neural network to study the performance of jig for beneficiation of non-coking coal.” Fuel, 97. pp. 151–156. doi:10.1016/j.fuel.2012.02.018
   Web of Science ®Google Scholar
• Raman, G. S. S., 2017. Evaluation and modeling of pressure filtration of coal refuse slurry. PhD Dissertation, The Pennsylvania State University, University Park, PA.
   Google Scholar
• Raman, G. S. S., and Klima, M. S., 2017a, “Application of artificial neural networks for evaluating pressure filtration of coal refuse slurries.” Mineral Processing and Extractive Metallurgy Review, 38. pp. 47–53. doi:10.1080/08827508.2016.1244060
   Web of Science ®Google Scholar
• Raman, G. S. S., and Klima, M. S., 2017b, “Evaluation of pressure filtration of coal refuse slurry: a fractional factorial design approach.” International Journal of Coal Preparation and Utilization, pp. 1–13. doi:10.1080/19392699.2017.1329726
   Google Scholar
• Raman, G. S. S., Klima, M. S., and Bishop, J. M., 2017, “Pressure filtration: bench-scale evaluation and modeling using multivariable regression and artificial neural network.” International Journal of Mineral Processing, 158. pp. 76–84. doi:10.1016/j.minpro.2016.11.020
   Web of Science ®Google Scholar
• Riedmiller, M., and Braun, H., 1993, “A direct adaptive method for faster backpropagation learning: the RPROP algorithm.” IEEE International Conference on Neural Networks, 1. pp. 586–591. doi:10.1109/ICNN.1993.298623
   Google Scholar
• Singh, V., 2013, “Artificial neural network modeling of ball mill grinding process.” Journal of Powder Metallurgy and Mining, 2. pp. 2–5. doi:10.4172/2168-9806.1000106
   Google Scholar
• Stickland, A. D., de Kretser, R. G., Scales, P. J., Usher, S. P., Hillis, P., and Tillotson, M. R., 2006, “Numerical modelling of fixed-cavity plate-and-frame filtration: fomulation, validation and optimisation.” Chemical Engineering Science, 61. pp. 3818–3829.
   Web of Science ®Google Scholar
• Teoh, S. K., 2003. Studies in filter cake characterisation and modelling. PhD Dissertation, National University of Singapore, Singapore.
   Google Scholar
• Verma, S., and Klima, M. S., 2010, “Evaluation of a pilot-scale, plate-and-frame filter press for dewatering thickener underflow slurries from bituminous coal-cleaning plants.” International Journal of Coal Preparation and Utilization, 30. pp. 1–19. doi:10.1080/19392691003776392
   Web of Science ®Google Scholar
• Yanxiang, W., and Wang, B., 2004, “Analysis of the medium resistance for constant pressure filtration.” Chinese Journal of Chemical Engineering, 12. pp. 33–36.
   Web of Science ®Google Scholar