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This study aims to investigate the recovery of copper from a low-grade source of an ore deposit in sulphuric acid solution. In this study, a full-factorial statistical design model and Artificial Neural Networks (ANN) are applied to develop an analytical approach to understand the dissolution parameters better. Temperature, time, particle size, and acid concentration were selected as the leaching parameters. The experimental design set by the full-factorial model was again trained with the ANN model to compare the predicted results. The correlation coefficients (R 2 ) in both the models are 0.9962 and 0.99815, respectively, indicating both the models offered a well-aligned explanation for the variability of the experimental values to the predicted values. Therefore, the outcome of this study demonstrates that the proposed design model results are well-aligned with the experimental results, and 88% of copper metal can be recovered at a condition of 120 minutes, 90°C temperature, the particle size of -75 + 63 µm, and acid concentration of 1M.

Experimental and statistical modelling on copper recovery from low-grade mixed sulphide-oxide ore by direct leaching