Computer modeling of high-pressure leaching process of nickel laterite by design of experiments and neural networks

Abstract

Due to the complex chemical composition of nickel ores, the requests for the decrease of production costs, and the increase of nickel extraction in the existing depletion of high-grade sulfide ores around the world, computer modeling of nickel ore leaching process became a need and a challenge. In this paper, the design of experiments (DOE) theory was used to determine the optimal experimental design plan matrix based on the D optimality criterion. In the high-pressure sulfuric acid leaching (HPSAL) process for nickel laterite in “Rudjinci” ore in Serbia, the temperature, the sulfuric acid to ore ratio, the stirring speed, and the leaching time as the predictor variables, and the degree of nickel extraction as the response have been considered. To model the process, the multiple linear regression (MLR) and response surface method (RSM), together with the two-level and four-factor full factorial central composite design (CCD) plan, were used. The proposed regression models have not been proven adequate. Therefore, the artificial neural network (ANN) approach with the same experimental plan was used in order to reduce operational costs, give a better modeling accuracy, and provide a more successful process optimization. The model is based on the multi-layer neural networks with the back-propagation (BP) learning algorithm and the bipolar sigmoid activation function.

Keywords

nickel laterite / leaching / computer simulation / design of experiments (DOE) / response surface method (RSM) / neural networks

References

[1] Whittington B.I., Muir D. Pressure acid leaching of nickel laterites. Miner. Process. Extr. Metall. Rev., 2000, 21, 527

[2] Habashi F. Pressure hydrometallurgy: Key to better and nonpolluting process, Part 1. Eng. Min. J., 1971, 172(2): 96.

[3] Habashi F. Pressure hydrometallurgy: Key to better and nonpolluting process, Part 2. Eng. Min. J., 1971, 172(5): 88.

[4] Habashi F. Taylor P.R. Laboratory Autoclaves for Hydrometallurgical Research, Extraction & Processing Division, 2000, Nashville, The Minerals, Metals, & Materials Society, 411.

[5] G.P. Tindall, High Temperature Acid Leaching of Western Australian Laterites [Dissertation], Murdoch University, 1998, p.214.

[6] Vucurovic D.R. Study of Possibility of Nickel Valorization from Its Ore in Serbia, 1983, Belgrade, Faculty of Technology and Metallurgy, 271.

[7] Stopic S., Friedrich B., Fuchs R. Kinetics of leaching of nickel lateritic ore at an atmospheric pressure. J. Metall., 2003, 7, 235.

[8] Stopic S., Friedrich B., Fuchs R. Sulphuric acid leaching of the Serbian nickel lateritic ore. Erzmetall, 2003, 56, 204.

[9] Stopic S. High pressure leaching of nickel lateritic ore. Humboldt Kosmos, 2003, 82, 34.

[10] S. Stopic, B. Friedrich, and N. Anastasijevic, Kinetics of high pressure leaching of the Serbian nickel lateritic ore, [in] Proceeding of EMC 2003, Volume 1, Copper and Nickel, Hanover, 2003, p.189.

[11] Stopic S., Friedrich B., Anastasijevic N., Onija A. Experimental design approach regarding kinetics of high pressure leaching processes. J. Metall., 2003, 9, 273.

[12] Lenth R.V. Quick and easy analysis of unreplicated factorials. Technometrics, 1989, 31, 469

[13] Leparoux M., Loher M., Schreuders C., Siegmann S. Neural network modeling of the inductively coupled RF plasma synthesis of silicon nanoparticles. Powder Technol., 2008, 185, 109

[14] Li D., Park K.H., Wu Z., Guo X.Y. Response surface design for nickel recovery from laterite by sulfation-roasting-leaching process. Trans. Nonferrous Met. Soc. China, 2010, 19(Suppl.1): 92

[15] Ezugwi E.O., Fadore D.A., Bonney J., Silva K.B. D., Sales W.F. Modeling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using artificial neural network. Int. J. Mach. Tool. Manuf., 2005, 45, 1375

[16] Younesi M., Bahrololoom M.E., Ahmadzadeh M. Prediction of wear behaviors of nickel free stainless steel-hydroxyapatite bio-composites using artificial neural networks. Comput. Mater. Sci., 2010, 47, 645

[17] Rashidi A.M., Eivani A.R., Amadeh A. Application of artificial neural networks to predict the grain size of nano-crystalline nickel coating. Comput. Mater. Sci., 2009, 45, 499

[18] Chou E.C., Queneau P.B., Rickard R.S. Sulfuric acid pressure leaching of nickeliferous limonites. Metall. Trans. B, 1977, 8, 547.

[19] Georgiou D., Papangelakis V.G. Sulphuric acid pressure leaching of a limonitic laterite: chemistry and kinetics. Hydrometallurgy, 1998, 49, 23

[20] Fisher R. Design of Experiments, 1974 9th ed. Hafner, New York, Reprinted

[21] Box G.E.P., Wilson K.B. On the experimental attainment of optimum conditions. J. Roy. Statist. Soc. Ser. B, 1951, 13, 1.

[22] J. Stanic, Methods of Engineering Measurements [Dissertation], Faculty of Mechanical Engineering, University of Belgrade, 1990.

[23] M. Milivojevic, Mathematical Modeling of Real Objects, Processes and Systems by Means of Central Compositional Symmetric Experimental Plans [Dissertation], Faculty of Mechanical Engineering, University of Belgrade, 1996.

[24] Milivojevic M., Drndarevic D. Package FIDIJA, Design of Experiments & Regression and Dispersion Analyses Package, 1996, Uzice, Serbia, High Technical and Business School

[25] Software MODDE 5.0, Version 5.0, Umetrics AB, USA, 1999.

[26] Clelland J.L. M., Rumelhalt D.E. Explorations in Parallel Distributed Processing, 1988, Cambridge, MIT Press

[27] D. Drndarevic, Modeling and Optimization of Powder Metallurgy Process by Neural Networks [Dissertation], Faculty of Mechanical Engineering, University of Belgrade, 1996.

[28] Z.S. Yu, Feed-Forward Neural Network and Their Application in Forecasting, Beyond Dream, 2001.

[29] Cruz J.A., Wishart D.S. Applications of machine learning in cancer prediction and prognosis. Cancer Inf., 2006, 2, 59.