Title of article :
Applying Softcomputing for Copper Recovery in Leaching Process
Author/Authors :
Leiva, Claudio Department of Chemical Engineering - Universidad Catolica del Norte, Antofagasta, Chile , Flores, Víctor Department of Computing & Systems Engineering - Universidad Catolica del Norte, Antofagasta, Chile , Salgado, Felipe Department of Chemical Engineering - Universidad Catolica del Norte, Antofagasta, Chile , Poblete, Diego Department of Chemical Engineering - Universidad Catolica del Norte, Antofagasta, Chile , Acuña, Claudio Department of Chemical and Environmental Engineering - Universidad Tecnica Federico Santa Mar , Chile
Pages :
7
From page :
1
To page :
7
Abstract :
The mining industry of the last few decades recognizes that it is more profitable to simulate model using historical data and available mining process knowledge rather than draw conclusions regarding future mine exploitation based on certain conditions. The variability of the composition of copper leach piles makes it unlikely to obtain high precision simulations using traditional statistical methods; however the same data collection favors the use of softcomputing techniques to enhance the accuracy of copper recovery via leaching by way of prediction models. In this paper, a predictive modeling contrasting is made; a linear model, a quadratic model, a cubic model, and a model based on the use of an artificial neural network (ANN) are presented. The model entries were obtained from operation data and data of piloting in columns. The ANN was constructed with 9 input variables, 6 hidden layers, and a neuron in the output layer corresponding to copper leaching prediction. The validation of the models was performed with real information and these results were used by a mining company in northern Chile to improve copper mining processes.
Keywords :
Applying Softcomputing , softcomputing techniques , artificial neural network (ANN) , copper mining processes.
Journal title :
Scientific Programming
Serial Year :
2017
Full Text URL :
Record number :
2607665
Link To Document :
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