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Man Yang,Yun Huang,Haijun Cao,Yuanhua Lin,Amin Song,Qingyuan Sun 한국고분자학회 2017 폴리머 Vol.41 No.3
A novel organic/inorganic hybrid TVE-resin containing SiO₂ structure for phenol removal is successfully prepared by dispersion polymerization, in which the inorganic phase is composed of nano-SiO₂ modified by vinyltrimethoxy silane (VTMS) and the organic phase is constituted of ethylene glycol dimethacrylate (EGDMA). The chemical structure and physical properties of TVE-resin are characterized by FTIR, TGA, SEM and BET. The adsorption studies imply that the novel materials show optimum adsorption capacity at pH=6, dose 0.1 g, contact time 60 min, initial concentration 3000 mg/L and room temperature. The pseudo first-order model can be well fitted with the kinetic process. According to the adsorption isotherm analysis, the Freundlich model gives a better fit to the experimental data, indicating a multiple molecular adsorption for phenol removal. The calculated thermodynamic parameters indicate an exothermic and spontaneous process. A mixture desorption solvent containing methanol and deionized water (v:v, 1:1) can regenerate the TVE-resin completely and the resin displays good reusability after five regeneration recycles.
Man Yang,Yilun Zou,Lei Ding,Yang Yu,Jinai Ma,Lei Li,Ande Fudja Rafryanto,Jing Zou,Arramel,Haitao Wang 한국탄소학회 2023 Carbon Letters Vol.33 No.5
Decabromodiphenyl ether (BDE209) is a persistent aromatic compound widely associated with environmental pollutants. Given its persistence and possible bioaccumulation, exploring a feasible technique to eradicate BDE209 efficiently is critical for today’s environmentally sustainable societies. Herein, an advanced nanocomposite is elaborately constructed, in which a large number of titanium dioxide ( TiO2) nanoparticles are anchored uniformly on two-dimensional graphene oxide (GO) nanosheets ( TiO2/GO) via a modified Hummer’s method and subsequent solvothermal treatment to achieve efficient photocatalytic degradation BDE209. The obtained TiO2/ GO photocatalyst has excellent photocatalytic due to the intense coupling between conductive GO nanosheets and TiO2 nanoparticles. Under the optimal photocatalytic degradation test conditions, the degradation efficiency of BDE209 is more than 90%. In addition, this study also provides an efficient route for designing highly active catalytic materials.
Probability Prediction Method of Short‑Term Electricity Price Based on Quantile Neural Network Model
Zhaoyang Qu,Manyang Gao,Yuqing Liu,Hongbo Lv,Jian Sun,Miao Li,Wei Liu,Mingshi Cui 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.2
Aiming at the inaccuracy of short-term electricity price forecasting in competitive power markets, a probabilistic short-term electricity price forecasting method based on the quantile neural network model is proposed. First, a method for selecting electricity price similarity based on comprehensive infuencing factors is designed to select the forecast data set with similar characteristics to the forecast date. The similar daily quantile regression algorithm is then combined with the generalized dynamic fuzzy neural network to construct a quantile neural network electricity price model for obtaining the predicted daily electricity price condition quantile. Finally, the kernel density function is used to convert the predicted daily electricity price condition quantile into the predicted probability density curve to realize short-term electricity price probability prediction. The data of the electricity market of the city of Dayton, Ohio in the United States is used as an example. The experimental results demonstrate that the proposed method can efectively improve the accuracy of short-term electricity price forecasting
Zhaoyang Qu,Wanxin Wang,Nan Qu,Yuqing Liu,Hongbo Lv,Kewei Hu,Jianyou Yu,Manyang Gao,Jiajun Song 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.4
In order to improve the accuracy of forecasts of the electricity sales of power sales companies, a depth forecast model of electricity sales based on the characteristics of the power market is proposed. First, based on survival analysis, the calculation method of the user churn rate in the electricity market is given, and the number of users at a certain moment in the future is predicted. Then, users’ electricity consumption that calculated by the deep belief network and the predicted quantity of users are combined to design a forecast model of electricity sales. Finally, the model is solved utilizing the weighting algorithm of adaptive inertia. The analysis of the example shows that the proposed method achieves a signifi cant improvement in the accuracy of power sales forecasting.
Zhaoyang Qu,Wanxin Wang,Nan Qu,Yuqing Liu,Hongbo Lv,Kewei Hu,Jianyou Yu,Manyang Gao,Jiajun Song 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.6
Due to unfortunate mistake the grant numbers have been omitted in the acknowledgments section: This work is supported by the National Natural Science Foundation of China (No. 51437003), Jilin Province Science and Technology Development Plan Project of China (20160623004TC, 20180201092GX), Jilin Science and Technology Innovation Development Plan Project of China (201830817).