Currently, there have been issues concerning the depletion and scarcity of mineral resources. This is mostly due to the excavation of high grade minerals having already occurred years and years ago, hence forcing the mining industry to opt for the production and optimization of lower grade minerals. This however brings about a plethora of problems, many of which economic, stemming from the purification of those low grade minerals in various stages required for mineral processing. In order to reduce costs and aid in the optimization of the mining stream, this study, introduces an automatic mineral identification system which combines the predictive abilities of deep learning with the excellent resolution of hyperspectral imaging, for pre-stage of mineral processing. These technologies were used to identify and classify high grade arsenic (As) bearing minerals from their low grade mineral counterparts non-destructively. Most of this ability to perform such tasks comes from the highly versatile machine learning model which employs deep learning as a means to classify minerals for mineral processing. Experimental results supported this statement as the model was able to achieve an over 90% accuracy in the prediction of As-bearing minerals, hence, one could conclude that this system has the potential to be employed in the mining industry as it achieves modern day system requirements such as high accuracy, speed, economic, userfriendly and automatic mineral identification.
A new Ti smelting process via. Bi–Ti alloy is proposed. This process comprises reduction of TiCl4 to Bi–10 mol%Ti alloy by Bi–Mg alloy, precipitation of Ti-rich compound from the alloy, and vacuum distillation. In this study, we investigated the precipitation and distillation processes. In the precipitation process, the Bi–10 mol%Ti liquid alloy is cooled from 900 ℃ to 500 ℃ to precipitate Bi9Ti8 in the liquid alloy. The Bi9Ti8 is recovered by a two-step separation method: recovery of mixture of Bi9Ti8 and Bi and further removal of Bi by centrifugal filtration. We demonstrated the recovery of mixture. As the results, Ti concentration in the mixture was 31 mol%, and the Ti yield was about 45 %. Because the remained liquid alloy after the recovery contains a large amount of Bi9Ti8, it is required to reuse the remained alloy in the precipitation process. Assuming the reuse of remained alloy, the material flow of the process was designed based on the experimental results. The centrifugal filtration of the mixture of Bi9Ti8 and Bi was also carried out at 500 ℃. By the centrifugal filtration at 50 G, alloys with a size of 1.5 mm were obtained, and the Ti concentration in the alloys was increased from 31 mol% to 40 mol%. Vacuum distillation of alloy powder and ingot was demonstrated. The distillation rate was enhanced when using the powder than when using alloy ingot as a starting material.
In this study, pressure response at A monitoring well in early stage of CO2 geological storage were predicted against pressure build-up at CO2 injector after starting CO2 injection into a deep saline aquifer to design the monitoring well distance from the injector and resolution or sensitivity of a pressure transmitter installed in the monitoring well. The numerical simulations on pressure distributions and expanding CO2 plume front were carried out using a reservoir simulator, CMG-STARS, for the aquifer (10 km in radius, 50m in height) with open boundary under 1,000 m from the ground or seabed level. The ratio of pressure response at the monitoring well against the pressure build-up at the injector have been presented for various monitoring locations (500 to 5,000 m from a injector) and homogeneous and heterogeneous models of horizontal permeability distribution in the aquifer (Base Model and Model 1, 2 & 3) and CO2 injection patters during 100 days (Scheme 1, 2 & 3). It has been presented from the numerical simulation results that the monitoring well radial distance from the injector is recommended to be 2,000 to 4,000 m or less than 1000 m when the resolution or sensitivity of the pressure transmitter is 1kPa or 10kPa, respectively.