Integration of experimental study and neural network modeling for estimating iron recovery in Davis tube tests

Abstract

Abstract Magnetic separation is a common procedure for the enrichment of magnetic iron ores. Davis tube (DT) test is a standard laboratory technique used to determine the optimum magnetic recovery of iron ore using wet low-intensity magnetic separators. However, the DT test is time-consuming and labour-intensive. In this study, based on the results of DT-tests, generalized regression (GRNN) and radial basis function (RBF) neural networks were developed to predict iron recovery through the Fe and FeO content of the feed. First, the DT tests were performed on 613 iron ore samples with varying Fe and FeO content. Then, neural networks were used to model the iron recovery from the DT test, using the Fe and FeO content of the feed as input data. The modeling results showed that GRNN is a better model for predicting iron recovery. The main statistical metrics indicated that GRNN has AAPRE, RMSE, and R2 values of 3.929%, 2.804, and 0.976 respectively for a total of 613 data points. Moreover, sensitivity analysis demonstrated that iron recovery is directly influenced by both Fe and FeO contents, with FeO content having a more pronounced effect. Finally, Leverage analysis showed that GRNN is highly reliable for predicting iron recovery, with only 2.77% of data points flagged as suspicious based on outlier estimation.

Keywords

• Iron recovery
• Davis tube
• Magnetic separation
• Modeling
• Artificial neural networks