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Chemical treatment is the best technology for the purification of copper-cobalt aqueous solutions because of its ability to remove suspended solids detrimental to downstream processes. However, the lack of optimization and adaptation of this method for the purification of the solutions obtained from the leaching of copper-cobalt ores with high mineralogical variability leads to significant fluctuations in the efficiency of the purification. This work investigated the batch settlingflocculation of fine solid particles (Al 2 O 3 and SiO 2) from copper-cobalt aqueous solutions using different flocculants (Brontë 234, APAM D8625-10, and CPAM D9640). The experimental variables comprised flocculant type, flocculant dosage, solids concentration, settling area, settling rate, % Al 2 O 3 , % SiO 2 , and particle size. The experimental 12 9 7 matrix was analyzed by principal component analysis, and the resulting principal components (PCs) and Varimax rotated PCs were analyzed using correlation circle plots. The most important settling variables proved to be the solids concentration, together with % Al 2 O 3 and particle size. High settling rate (0.42 m/h) and low settling surface (0.40 m 2 /t/h) were obtained at the flocculant dosage of 20 g/t. In addition, good settling performance was obtained with anionic flocculants (APAM D8625-10 and Bronté 234) rather than the cationic flocculant considered (CPAM D9640). Keywords Multivariate analysis Á Principal component analysis Á Settling Á Flocculation Á Cu-Co ores Á Leaching Á Concentrates The contributing editor for this article was Gabrielle Gaustad.

While the uncertainty brought about by a varying feed mineralogy was taken into consideration, the paper investigated the modeling and prediction of the leaching behavior of complex copper-cobalt bearing ores, using an artificial neural network (ANN) with a backforward algorithm. The process optimization is further conducted using the response surface methodology (RSM) employing the Box-Behnken design (BBD). Seven (7) parameters were considered in a multiple linear regression according to the L12 screening plan (27) of Plackett–Burman. From the seven parameters, four including solid percentage (15, 27.5, 40%), time (45, 90, 135 min), particle size passing (53, 75, 105 µm), and Fe2+ ion concentration (2, 4, 6 g/L) are modeled with L27(34) BBD. With a composite desirability of 0.94, leaching yields of 93.46% Cu and 89.43% Co were obtained. The neural network algorithm used is the BFGS (Broyden, Fletcher, Goldfarb and Shanno) algorithm multilayer perceptron with the hyperbolic tangent activation function for the hidden layer and a linear activation function for the neural output. The Multilayer perceptron {4–7-1} structure was chosen as a suitable arrangement for Cu leaching. Comparing the predicted values and those obtained experimentally resulted with a correlation coefficient of 0.9552 for the data trained in the artificial neural network and 0.8742 for the data obtained with the response surface methodology. The synergy of these 2 techniques shows that the prediction can be achieved by means of the ANN giving the values of the root mean square errors (RMSE) of 0.0115, 0.00624, 0.0229, respectively, for the training, testing and validation sets for copper recovery while the correlational study between variables could be done through the RSM. The above includes only the 95% confidence interval while the remaining 5% would be uncertain. The above results and conclusion are accompanied by the relative uncertainty as the ore mineralogy varies. The combination of the synergistic use of ANN and RSM with the sensitivity analysis has approached the process to the physics of the Multi-criteria decision-making. Graphical Abstract