Neural net based knowledge extraction from the historical data of an industrial leaching process

Hydrometallurgy

Volumn 43, Issue 1-3, 1996, Pages 95-116

  Rademan, J A M ^a   Moolman, D W ^a   Lorenzen, L ^a   Van Deventer, J S J ^a,b   Aldrich, C ^a  

^a STELLENBOSCH UNIVERSITY   (South Africa)

^b UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ACQUISITION; HISTORY; KNOWLEDGE BASED SYSTEMS; MATHEMATICAL MODELS; NEURAL NETWORKS; PERFORMANCE; PROCESS ENGINEERING; STATISTICAL METHODS;

LEACHING;

EID: 0030286114     PISSN: 0304386X     EISSN: None     Source Type: Journal    
DOI: 10.1016/0304-386x(95)00115-w     Document Type: Article

References (20)

1. Hofirek, Z. and Kerfoot, D.G.E., The chemistry of the nickel-copper matte leach and its application to process control and optimisation. Hydrometallurgy, 29 (1992): 357-381.
2. Symens, R.D., Queneau, P.B., Chou, E.C. and Clark, F.F., Leaching of iron-containing copper-nickel matte at atmospheric pressure. Can. Metall. Q., 18 (1979): 145-153.
3. 2 leaching of nickel-copper matte, Can. Metall. Q., 26 (1987): 265-276.
4. Rademan, J.A.M., Lorenzen, L. and Van Deventer, J.S.J., An off-line computer simulation for controlling the repulp section in a plant, Miner. Eng., 8(6) (1995): 679-696.
5. Walpole, R.E. and Myers, R.H., Probability and Statistics for Engineers and Scientists. New York, 4th ed. (1989), pp. 79-104 and 391-397.
6. Pandit, S.M. and Wu, S.-M., Time Series and System Analysis with Applications. Wiley, New York (1983), pp. 1-4 and 112-115.
7. Lippmann, R.P., An introduction to computing with neural nets. IEEE ASSP Mag., 35 (1987): 4-22.
8. Wasserman, P.D., Neural Computing - Theory and Practice. Van Nostrand Reinhold, New York (1989).
9. Maren, A., Harston, C.T. and Pap, R.M., Handbook of Neural Computing Applications. Academic Press, Harcourt Brace Jovanovich, San Diego (1990).
10. Hinton, G.E., How neural networks learn from experience. Sci. Am., 267(3) (1992); 105-127.
11. Chitra, S.P., Use neural networks for problem solving. Chem. Eng. Prog., 89(4) (1993): 44-52.
12. Bhat, N. and McAvoy, T.J., Use of neural nets for dynamic modeling and control of chemical process systems. Comp. Chem. Eng., 14(4/5) (1990): 573-583.
13. Hong-Te Su, McAvoy, T.J. and Werbos, P., Long-term prediction of chemical processes using recurrent neural networks: A parallel training approach. Ind. Eng. Chem. Res., 31 (1992): 1338-1352.
14. Reuter, M.A., Van der Walt, T.J. and Van Deventer, J.S.J., Modeling of metal-slag equilibrium processes using neural nets. Metall. Trans. B, 23B (1992): 643-650.
15. Van der Walt, T.J. and Van Deventer, J.S.J., The simulation of ill-defined metallurgical processes using a neural net training program based on conjugate-gradient optimization. IFAC Workshop on Expert Systems in Mineral and Metal Processing (Helsinki Univ. Technology, Espoo, Finland, 26-28 Aug., 1991).
16. Van der Walt, T.J., Van Deventer, J.S.J. and Barnard, E., The dynamic modeling of ill-defined processing operations using connectionist networks. Chem. Eng. Sci., 28(11) (1992): 1945-1958.
17. Karim, M.N. and Rivera, S.L., Comparison of feed-forward and recurrent neural networks for bioprocess state estimation. Europ. Symp. on Computer Aided Process Engineering - 1, ESCAPE-1 (Elsinore, Denmark). Suppl. to Comp. Chem. Eng. (1992), S369-S377.
18. Kramer, M.A. and Leonard, J.A., Diagnosis using back propagation neural networks - Analysis and criticism. Comp. Chem. Eng., 14(12) (1990): 1323-3338.
19. Neural Computing - A Technology Handbook for Professional II/PLUS and NeuralWorks Explorer. NeuralWare, Pittsburgh (1993).
20. Lippmann, R.P., Pattern classification using neural networks. IEEE Commun. Mag., 35 (1989): 4-21.