| 1 | Germanium separation and purification by leaching and precipitation显示文摘In this research work, extraction and purification of germanium from zinc leach residues(ZLR) were investigated. The results of ICP, XRF, and atomic adsorption spectroscopy(AAS) tests show that contents of germanium, iron, lead, and zinc within the leaching residue were 105×10^(-6), 3.53%, 10.35%, and 8.8%, respectively. XRD results indicate that the main minerals were in different forms of sulfates(CaSO_4·2H_2O, PbSO_4 and ZnSO_4·6H__2O), silicate(SiO_2), and oxide(Fe_2O_3). Dissolution of leaching filter cake was carried out using 5 parameters and each in 4 levels(acid concentration, temperature, time, liquid-to-solid ratio, and stirring speed) by Taguchi method(L_(16)), and then optimization of the effective parameters by response surface method. Under optimum conditions, zinc and germanium dissolution efficiencies were 88.71% and 8%, respectively. Leaching tests with sulfuric acid(added di-ammonium oxalate monohydrate) and hydrochloric acid(HCl) on the residues obtained from previous-stage sulfuric acid dissolution, yielded germanium and iron recoveries of 83%, 88%, 40%, and 90%, respectively. Thus, leaching experiment with sulfuric acid(added di-ammonium oxalate monohydrate) was superior to that with hydrochloric acid due to high and low extraction amounts of germanium and iron, respectively. Precipitation experiments revealed that germanium purification with tannic acid presented a better result compared to sodium hydroxide and ammonia. Under optimum conditions, contents of germanium and iron in the solution after precipitation were 0.1505% and 14.7% with precipitation yields of 91% and 52%, respectively. | Saeid Bayat Sajjad Aghazadeh Mohammad Noaparast Mahdi Gharabaghi Behrooz Taheri | 2016 | Journal of Central South University2016，23，9： | 3 |
| 3 | Comprehensive power swing detection by current signal modeling and prediction using the GMDH method显示文摘Power swing is an undesirable variation in power flow. This can be caused by large disturbances in demand load, switching, disconnection or reclosing lines. This phenomenon may enter the zones of distance relays and cause relay malfunction leading to the disconnection of healthy lines and undermining network reliability. Accordingly, this paper presents a new power swing detection method based on the prediction of current signal with a GMDH (Group Method of Data Handling) artificial neural network. The main advantage of the proposed method over its counterparts is the immunity to noise effect in signals. In addition, the proposed method can detect stable, unstable, and multi-mode power swings and is capable of distinguishing them from the variety of permanent faults occurring simultaneously. The method is tested for different types of power swings and simultaneous faults using DIgSILENT and MATLAB, and compared with some latest power swing detection methods. The results demonstrate the superiority of the proposed method in terms of response time, the ability to detect power swings of different varieties, and the ability to detect different faults that may occur simultaneously with power swings. | Seyed Amir Hosseini Behrooz Taheri Hossein Askarian Abyaneh Farzad Razavi | 2021 | Protection and Control of Modern Power Systems2021，6，1： | 0 |
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