Modeling the Temperature of the Evacuation Chamber with Artificial Neural Networks

Deynier Montero Gongora*, Ramon Alpajon Videaux and Keiler Cobas Cardoza

Received: September 21, 2018;   Published: September 27, 2018

DOI: 10.32474/ARME.2018.01.000107

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Abstract

This investigation approaches the artificial neural networks applied to the ore drying process in carbonate-ammonia leaching. To carry out this research, the main variables that characterize the process were identified. Besides, it was collected the data that comprise a whole month of facility´s operation. Furthermore, it was developed a regression analysis backwards, step by step, which allowed to determine that the linear correlation coefficient did not reach values higher than 0,62. In addition, it was pinpointed a two layered feed - forward back propagation neural network to model the temperature. Thins one reached the correlation coefficient values of 0,97 during its training and 0,95 in validation, as well as 0,87 in its generalization.

Keywords: Artificial Neuronal Network; Regression; Feed-Forward Backpropagation; Mineral Drying

Abstract| Introduction| Materials and Methods| Results and Discussion| Conclusion| References|