Themining industry of the last few decades recognizes that it is more profitable to simulatemodel using historical data and available\nmining process knowledge rather than draw conclusions regarding future mine exploitation based on certain conditions. The\nvariability of the composition of copper leach piles makes it unlikely to obtain high precision simulations using traditional statistical\nmethods; however the same data collection favors the use of softcomputing techniques to enhance the accuracy of copper recovery\nvia leaching by way of prediction models. In this paper, a predictive modeling contrasting is made; a linear model, a quadratic\nmodel, a cubic model, and a model based on the use of an artificial neural network (ANN) are presented. The model entries were\nobtained from operation data and data of piloting in columns. The ANN was constructed with 9 input variables, 6 hidden layers,\nand a neuron in the output layer corresponding to copper leaching prediction.The validation of the models was performed with\nreal information and these results were used by a mining company in northern Chile to improve copper mining processes.
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