Recovery and Grade Accurate Prediction of Pilot Plant Flotation Column Concentrate: Neural Network and Statistical Techniques

Abstract

In this study, the metallurgical performance (grade and recovery) forecasting of pilot plant flotation column using Artificial Neural Networks (ANN) and Multivariate Non-Linear Regression (MNLR) models is investigated. Modeling is performed based on 90 datasets at different operating conditions. The values of chemical reagents dosage, froth height, air and wash water flow rates, gas holdup and Cu, Mo grades in the rougher feed and flotation column feed, column tail and final concentrate streams are used to the simulation by means of NN and MNLR. The model validation analysis demonstrates the capability of both models to predict Cu and Mo grades and recoveries for a wide range of operating conditions in pilot flotation columns. It must be noted that ANN approach offers superior predictive capability over statistical method. It was also found that the error in prediction of metallurgical performance using the NN model was less than the error of the regression model. The best network is proposed with multi-layer perceptron (MLP) model, sigmoid activation function and Levenberg-Marquardt learning rule with 8-12-8-2 and 8-9-12-2 architectures, in order to estimate metallurgical performance of Cu and Mo respectively in flotation column. The results of this study indicate that a back-propagation neural network model with Root Mean Square Errors (RMSE) of 0.68 and 0.02 for prediction of Cu and Mo grades and 0.48 and 1.16 for prediction of Cu and Mo recoveries respectively has a better performance than the statistical method. © 2012 Elsevier B.V. All rights reserved.

Department(s)

Mining Engineering

Keywords and Phrases

Artificial Neural Network; Flotation column; Grade; Multivariate Non-Linear Regression; Recovery

International Standard Serial Number (ISSN)

0301-7516

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

18 Jul 2012

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