• Proceedings of the IEEK Conference
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• 2001.10a
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• pp.337-341
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• 2001

A new method to extract gold from pyrite roster cinder, which combines ultra-fine-grinding with resin-in-pulp, has been studied in this paper. Compared with traditional leaching technology, it can short leaching time, avoid complex filter process, lower sodium cyanide consumption and increase gold recovery by 35%. During leaching, aluminium oxide ball was used as stirred medium, hydrogen peroxide as leaching aid and sodium hexametaphosphate as grinding aid. With the high efficiency and chemistry effect of ultra-fine-grinding, the leaching process was developed and the gold leaching rate may reach 88%. With AM-2 Б resin as abosorber and sulfocarbamide (TU) as eluent, gold was recovered from cyanide pulp by resin-in-pulp. AM-2 Б resin has good adsorbability in cyanide solution(pH=10). It was easy to elude gold from the loaded resin with 0.1㏖/L cholhydric acid and 1㏖/L sulfocabamide. The effect of contact time, temperature and acidity etc. on the gold absorption had been examined with static methods. The results showed that the adsorption and desorption of gold could both reach over 98%. The effects of flow rate of solution on dynamic adsorption and elution of gold had been examined with dynamic methods. Breakthrough curve and elution curve had been drawn in this paper. A mild condition was determined through a number of experiments: leaching time 2 hours, liquid solid ratio 4:1, sodium cyanide 3kg/t, hydrogen peroxide 0.05%, sodium hexametaphosphate 0.05%; adsorption time 30 minutes, temperature 10-3$0^{\circ}C$, resin($m\ell$) solid(g) ratio 1:10, eluent resin ratio 10-20:1, velocity of eluent $1.5m\ell$/min. Under the mild condition, the gold recovery may reach 85%.

• Journal of IKEEE
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• v.25 no.3
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• pp.430-436
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• 2021

The non-contact body temperature measurement system is one of the key factors, which is manage febrile diseases in mass facilities using optical and thermal imaging cameras. Conventional systems can only be used for simple body temperature measurement in the face area, because it is used only a deep learning-based face detection algorithm. So, there is a limit to detecting abnormal symptoms of the infants and young children, who have difficulty expressing their opinions. This paper proposes an improved facial expression recognition algorithm for detecting abnormal symptoms in infants and young children. The proposed method uses an object detection model to detect infants and young children in an image, then It acquires the coordinates of the eyes, nose, and mouth, which are key elements of facial expression recognition. Finally, facial expression recognition is performed by applying a selective sharpening filter based on the obtained coordinates. According to the experimental results, the proposed algorithm improved by 2.52%, 1.12%, and 2.29%, respectively, for the three expressions of neutral, happy, and sad in the UTK dataset.