Predicting Optimized Dissolution of Selected African Copperbelt Copper-cobalt-bearing Ores by Means of Neural Network Prediction and Response Surface Methodology Modeling

Abstract

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

Bayesian statistics study of a sustainable dissolution of cobalt‐bearing minerals from Cu‐Co ores

“…On the other hand, several approaches and techniques for modelling Co leaching have been used. These include purely mathematical approaches such as the response surface methodology, [ 17 ] statistical approaches such as that of Taguchi, [ 9 ] principal component analysis [ 4 ] or artificial intelligence approaches such as artificial neural networks. [ 10,18 ] In the field of mineral processing, many mathematical or statistical models and algorithms have been used to predict and/or simulate the dissolution of minerals containing cobalt, however to date, no study has been devoted to probabilistic modelling, particularly with the Bayesian approach.…”

Agitation Leaching Behavior of Copper–Cobalt Oxide Ores from the Democratic Republic of the Congo

“…In general, owing to the growing number of applications of copper and cobalt in batteries and energy storage technology, their demand is increasing on a yearly basis [1]. Thus, as one of the largest producers of copper and cobalt in the world, the Democratic Republic of the Congo is known to have much complexity and diversity in its coppercobalt-bearing ores [2,3]. The development and utilization of copper-cobalt ore is mainly in the Central African copper-cobalt metallogenic belt, which holds 10% of the world's copper and 70% of the cobalt resources [4].…”

Supervised Training and Unsupervised Machine Learning Testing for Cobalt Dissolution from Complex Cu-Co Ores in Acid Lixiviants

“…With more developments on electric cars, the demand for cobalt will rise exponentially, moreover cobalt will be essential in the development of lithium-ion batteries, as it plays a major role in the functionality of the battery [2]. As a result, this necessitates more research work on the extraction of this commodity to find more economical and environmentally friendly processing routes or conditions [2]- [4]. The RD-Congolese Copperbelt hosts one of the largest Cu and Co reserves in the world, with varying mineralogy from region to region, frankly in some cases from the same deposit [1].…”