Extraction of copper from copper-bearing biotite by ultrasonic-assisted leaching

Copper-bearing biotite is a refractory copper mineral found on the surface of the Zambian Copperbelt.Biotite is a copper oxide from which copper is extracted by various methods,especially by leaching.Leaching is the process of extracting a substance from a solid material dissolved in a liquid.To improve the efficiency of the leaching process by a more effective method,a new method called ultrasonic-assisted acid leaching is proposed and applied in this study.Compared with regular acid leaching,the ultrasound method reduced the leaching time from 120 to 40 min,and sulfuric acid concentration reduced from 0.5 to 0.3 mol·L^(-1).Besides,leaching temperature could be reduced from 75 to 45°C at the leaching rate of 78%.The mechanism analysis indicates that an ultrasonic wave can cause the delamination of a copper-bearing biotite and increase its specific surface area from 0.55 to 1.67 m^(2)·g^(-1).The results indicate that copper extraction from copper-bearing biotite by ultrasonic-assisted acid leaching is more effective than regular acid leaching.This study proposes a promising method for recycling valuable metals from phyllosilicate minerals.

Preparation of nanometer yellow iron and its UV absorption capacity

The nanometer yellow iron oxide was prepared by oxidizing Fe（OH）2 with air, which was verified with XRD and TEM. The result shows that nanometer yellow iron oxide is spindle-shaped and well-distributed with a long axis of 150-200 nm and short axis of 40-50 nm. Ultraviolet （UV） transmittance of the iron oxide shows the great effect of concentration on both transparency and UV ab- sorption, and it has been proven that iron oxide with a concentration of 0.025wt% is preferred. The spectrum of XRD indicates that it is goethite. When the yellow iron is dispersed in sol, given that the wavelength of UV is less than 300 nm, its UV absorption capacity is superior to those of ZnO and TiO2. The absorption capacity of the yellow iron is less than TiO2 and more than ZnO as the wavelength of UV is 300-400 nm.

Optimum Design of Movable Screen Jig Cycle

The basic method for designing and calculating reasonable jig cycle of movable screen jig was presented. The reasonabilityand reliability of designed jig cycle were verified by' means of the test on artificial mixed materials. An iron ore (12-100 mm) was treatedin tile TJD-75 model movable screen jig with the jig cycle. The good results were achieved. This would provide a reliable basis for designof commercial movable-screen jig.

Enhancing XRF sensor-based sorting of porphyritic copper ore using particle swarm optimization-support vector machine(PSO-SVM)algorithm

X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifications and hindering widespread technology adoption.Accurate classification models are crucial to determine if actual grade exceeds the sorting threshold using localized XRF signals.Previous studies mainly used linear regression(LR)algorithms including simple linear regression(SLR),multivariable linear regression(MLR),and multivariable linear regression with interaction(MLRI)but often fell short attaining satisfactory results.This study employed the particle swarm optimization support vector machine(PSO-SVM)algorithm for sorting porphyritic copper ore pebble.Lab-scale results showed PSO-SVM out-performed LR and raw data(RD)models and the significant interaction effects among input features was observed.Despite poor input data quality,PSO-SVM demonstrated exceptional capabilities.Lab-scale sorting achieved 93.0%accuracy,0.24%grade increase,84.94%recovery rate,57.02%discard rate,and a remarkable 39.62 yuan/t net smelter return(NSR)increase compared to no sorting.These improvements were achieved by the PSO-SVM model with optimized input combinations and highest data quality(T=10,T is XRF testing times).The unsuitability of LR methods for XRF sensor-based sorting of investigated sample is illustrated.Input element selection and mineral association analysis elucidate element importance and influence mechanisms.