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RISS 인기검색어
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- 저자 : Bi-Bin Huang (검색결과 2 건)
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Photodegradation of 17α-ethynylestradiol in nitrate aqueous solutions
Dong Ren,Tingting Bi,Shumei Gao,Xukun Li,Bin Huang,Xuejun Pan 대한환경공학회 2016 Environmental Engineering Research Vol.21 No.2
17α-Ethynylestradiol (Ee₂) has gotten growing concerns due to its widely detected in the environment and high estrogenic potency. However, the knowledge on the photochemical behaviors of Ee₂ in natural waters is still limited. Herein, the photodegradation and estrogenic potency variation of Ee₂ induced by nitrate were studied using a sunlight simulator consisted by a 300 W medium pressure mercury lamp and 290 nm cut-off filters. It was found that Ee₂ could be photodegraded at a rate of 0.0193 h-1 in pure aqueous solutions, and the photodegradation of Ee₂ could be significantly promoted by nitrate. The photodegradation removal rate of Ee₂ was increased from 9% in Milli-Q water to 85% in 2.0 mM nitrate solutions. Reactive species scavenging experiments demonstrated that the photogenerated HO? contributed about 55% to Ee₂ degradation. Fe(III), Cl- and dissolved humic acid (DHA) could inhibit the photodegradation of Ee₂ by competing the incident light and photogenerated HO?, while HCO₃ - had no influence on Ee₂ photodegradation. Ee₂ was determined to be phototransformed into organic chemicals without estrogenic potency by GC-MS and MCF-7 cell proliferation toxicity tests. These findings could extend our knowledge on the photochemical behaviors of steroid estrogens and provide information for ecological risk assessment.
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Hong-Yu Long,Cheng-Yong Zhu,Bi-Bin Huang,Chang-hao Piao,Ya-Qing Sun 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.4
The purpose of this paper is to discuss how to solve the problem of on-line identifi cation of model parameters of Li-ion battery and on-line estimation of SOC. Based on the matlab/simulink platform, a fi rst-order RC equivalent circuit model of the battery is built, and a joint estimation algorithm of the model parameters and SOC of the lithium ion battery is designed based on the dynamic model, which is compared with the single adaptive Kalman fi lter algorithm (AEKF). The simulation results show that the proposed joint estimation algorithm can make eff ective online estimation and update of the battery model parameters and SOC. The average estimation error of SOC is less than 2.8%, the estimation accuracy is higher than that of adaptive Kalman fi lter, and its robustness level is relatively high.
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