Neural Networks and Support Vector Machine models applied to energy consumption optimization in semiautogeneous grinding

Chemical Engineering Transactions

Volumn 25, Issue , 2011, Pages 761-766

  Curilem, Millaray ^a   Acuña, Gonzalo ^b   Cubillos, Francisco ^b   Vyhmeister, Eduardo ^b  

^a UNIVERSIDAD DE LA FRONTERA   (Chile)

^b UNIVERSIDAD DE SANTIAGO DE CHILE   (Chile)

Author keywords

[No Author keywords available]

Indexed keywords

COMPOSITE STRUCTURES; ENERGY UTILIZATION; GRINDING (MACHINING); GRINDING MILLS;

ENERGY CONSUMPTION EQUIPMENT; ENERGY CONSUMPTION OPTIMIZATION; FIRST PRINCIPLE MODELING; NEURAL NETWORK (NN); NON-LINEAR AUTOREGRESSIVE WITH EXOGENOUS; NONLINEAR AUTOREGRESSIVE MOVING AVERAGE WITH EXOGENOUS INPUTS; ON-LINE INFORMATION; SUPPORT VECTOR MACHINE MODELS;

SUPPORT VECTOR MACHINES;

EID: 79956270845     PISSN: 22839216     EISSN: 22839216     Source Type: Journal    
DOI: 10.3303/CET1125127     Document Type: Conference Paper

References (15)

1. Acuna, G. and Curilem, M., 2009, Comparison of neural networks and support vector machine dynamic models for state estimation in semiautogeneous mills, Lecture Notes in Computer Science, LNCS, (ISI-Proceeding). Lecture Notes in Computer Science, 5845, 478-487.
2. Lameck, N. S., Kiangi, K. K. and Moys, M. H., 2006, Effect of grinding media shapes on load behavior and mill power in a dry ball mill, Minerals Engineering, 19, 1357-1361. (Pubitemid 44416577)
3. Leontaritis, I. J. and Billings, S. A., 1985, Input-output parametric models for non-linear systems; Part 1: deterministic non-linear systems; Part 2: stochastic non-linear systems, International Journal of Control, 45, 1985, 303-344.
4. Magne, L., Valderrama, W. and Pontt, J., 1997, Conceptual vision and state of the semiautogenous mill technology, Revista Minerales, Instituto de Ingenieros de Minas de Chile. 52(218).
5. Nerrand, O., Roussel, P., Personnaz, L. and Dreyfus, G., 1993, Neural networks and non-linear adaptive filtering: unifying concepts and new algorithms, Neural Computation, 5, 165-199.
6. Norgaard, M., 2003, www.mathworks.com/matlabcentral/fileexchange/authors/ 1119, 02-06-2011.
7. Norgate, T. and Haque, N., 2010, Energy and greenhouse gas impact of mining and mineral processing operations, Journal of Cleaner Production, 18, 266-274.
8. Salazar, J. L., Magne, L., Acuna, G. and Cubillos, F., 2009, Dynamic modeling and simulation of semi-autogenous mills, Minerals Engineering, 22, 70-77.
9. Scholkopf, B., Smola, A., Williamson, R. C. and Bartlett P. L., 2000, New support vector algorithms, Neural Computation, 12, 1083-121.
10. Suykens, J., 2010, www.esat.kuleuven.be/sista/lssvmlab, 02-06-2011.
11. Suykens, J., 2001, Nonlinear modeling and support vector machines, Proceedings of the 18thIEEE. Instrum. and Measurement Technol. Conf., Budapest, Hungary, 287-294. (Pubitemid 32884027)
12. Suykens, J., Van Gestel, T., De Brabanter, J., De Moor, B. and Vandewalle, J., 2002, Least Squares Support Vector Machines, World Scientific, Singapore.
13. Vapnik, V., 1995, The nature of statistical learning theory, Springer, Verlag, New York.
14. Wei, D. and Craig, I. K., 2009, Grinding mill circuits - A survey of control and economic concerns, International Journal of Mineral Processing, 90, 56-66.
15. Werbos, P. J., 1990, Backpropagation through time: what it does and how to do it, Proceedings of the IEEE, Vol 78(10), 1150-1560.