Estimating Of Etchant Copper Concentration in The Electrolytic Cell using Artificial Neural Networks

Abstract

In this paper, Artificial Neural Networks (ANN), which are known for their ability to model nonlinear systems, provide accurate approximations of system behavior and are typically much more computationally efficient than phenomenological models are used to predict the etchant copper concentration in the electrolytic cell in terms of electric potential, operating time, temperature of the electrolytic cell , ratio of surface area of poles per unit volume of solution and the distance between poles. In this paper 350 sets of data are used to trained and test the network.. The best results were achieved using a model based on a feedforword Artificial Neural Network (ANN) with one hidden layer and fifteen neurons in the hidden layer gives a very close prediction of the copper concentration in the electrolytic cell.