Artificial Neural Network Prediction of Silicon and Nickel recovery in Al-Si-Ni alloy Manufactured by Stir Casting

Document Type : Original Article

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Mining and petroleum Engineering Department,Faculty of Engineering, Al-Azhar University, Qena- Egypt

Abstract

Artificial neural network (ANN) is a non-linear statistical technique that being used to describe the behaviour of the materials. Al-Si-Ni alloy was prepared by the stir casting method using different optimum parameters as reaction time, temperature, Ni2O3/Al weight ratio, and Na2SiF6/Al wt. ratio. The artificial neural network is used in predicting the silicon and nickel recovery of these prepared alloys. The obtained experimental results are used to train the artificial neural network (ANN) and the temperature, Ni2O3/Al wt. ratio, and Na2SiF6 / Al wt. ratio are used as ANN's inputs. The used ANN consists of three layers; Input layer that includes 4 neurons and the hidden layer include 9 neurons, while the output layer contains 2 neurons. The Levenberg-Marquardt (LM) is used as the training function. Optimal mean square errors (MSE) for the ANN during predicting and estimating silicon and nickel recovery equal 0.0358, 0.0034, respectively, when reaction time is the variable and other parameters are kept constant, MSE equal 1.4007e-04, 1.3478e-04 when temperature is variable and other parameters are kept constant, MSE equal 1.3839e-04, 9.9891e-05 when Ni2O3/Al wt. ratio was the variable and other parameters are kept constant and finally MSE equal 0.0287, 0.0263 when Na2SiF6/Al wt. ratio is variable and other parameters are kept constant.

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