2026-10-07T07:57:28Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34004124 2026-09-27T01:50:29Z category_26830 category_26827 category_26842 item_type_3 month_year_09_2026

<b>Prediction of Optimized Chalcopyrite and Cobalt III Oxide Microwave Pre-processed System Solubilization Behavior in Acid Medium by Artificial Neural Network</b> Ntakamutshi Patrick Tshibanda (25115238) Mineral processing/beneficiation Hydrometallurgy Pyrometallurgy Prediction Microwave Cobalt III oxide solubilization artificial neural network and response surface methodology Hydrometallurgy Chalcopyrite <p dir="ltr">This study investigates the application of Artificial Neural Networks (ANNs) to predict the solubilization behavior of copper and cobalt from microwave-pretreated ores. Five different architectures of Multilayer Perceptron (MLP) ANNs – MLP 4-4-2, MLP 4-6-2, MLP 4-9-2 (two variants), and MLP 4-10-2 – were developed and compared. Input parameters included microwave power (280-700 W), treatment time (2-8 min), particle size (45-106 µm), and stoichiometric ratio (0-3), while copper and cobalt solubilization yields served as output variables during leaching of pre-processed ores at pH 1.5 in 2 hours.</p><p dir="ltr">Experimental results showed solubilization yields ranging from 4.16% to 58.92% for copper and 0% to 96.03% for cobalt. In optimal conditions, solubilization yield of 62.56% for copper was obtained after leaching of pre-processed sample at 700W, 4 minutes, 53 microns and heterogenite/chalcopyrite ratio of 1:1; while the yield of 85.94% for cobalt was obtained at 700 W, 6 minutes, 45 microns and 1:1 ratio. Sensitivity analysis revealed that the stoichiometric ratio was the most influential parameter, with an average relative importance of 11.52, followed by particle size (1.90), microwave power (1.93), and treatment time (1.33). The MLP 4-6-2 model demonstrated the best performance with correlation coefficients of 0.970 for training and 1.000 for test and validation sets.</p><p dir="ltr">The study concludes that ANNs constitute an effective tool for modeling and optimizing microwave-assisted leaching processes, with significant implications for the non-ferrous metal extractive industry.</p> 2026-09-27T01:50:29Z Dataset Dataset 10.6084/m9.figshare.34004124.v1 https://figshare.com/articles/dataset/_b_Prediction_of_Optimized_Chalcopyrite_and_Cobalt_III_Oxide_Microwave_Pre-processed_System_Solubilization_Behavior_in_Acid_Medium_by_Artificial_Neural_Network_b_/34004124 CC BY 4.0