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DFT study of the oxidation of Hg0 by O2 on an Mn-doped buckled g-C3N4 catalyst
Liu Shuai,Xu Mengxia,Chen Yipei,Mu Xueliang,Yu Jiahui,Yang Gang,Luo Xiang,Jiang Peng,Wu Tao 한국물리학회 2022 Current Applied Physics Vol.40 No.-
Due to the water-insoluble nature of Hg0, its oxidization to Hg2+, which is water-soluble, is a viable approach for its effective removal at coal-fired plants using existing flue gas desulfurization (FGD) unit. In this study, the adsorption and oxidation of elemental mercury on an Mn-doped g-C3N4 material were investigated. The spinpolarized density functional theory method was adapted to optimize the geometry structures and then to determine the corresponding electronic structures, while the CI-NEB method was adopted to search for the stable intermediates during the reaction(s). The analysis of energy and project density of states shows that the Mn-g- C3N4 exhibits an excellent affinity to Hg atoms. It is found that it is feasible for Hg atoms to oxidize on the Mn-g- C3N4 surface via two possible E-R paths, but with relatively high energy barriers. This research provides insights into a viable way for mercury removal using O2 as the oxidizing agent.
Reliability of Jack-up against Punch-through using Failure State Intelligent Recognition Technique
Tao Lyu,Changhang Xu,Guoming Chen,Yipei Zhao,Qingyang Li,Tantan Zhao 대한토목학회 2019 KSCE Journal of Civil Engineering Vol.23 No.3
The preload operation of jack-up in complex multi-layered foundation requires enhanced understanding of its behaviour in punchthrough accident and suitable safety analysis tools for the assessment of their reliability for a particular site. In this study, reliability analysis model of jack-up against punch-through is established considering structural uncertainty. In order to identify the failure state, an improved reliability solution method has been developed based on Sparse Auto-Encoder (SAE) deep learning network model. Sparse self-coding algorithm is used to the training of the deep network, and Softmax regression model is established to solve the identification and classification problem of the output layer. The first application of the technique was the study of HYSY 941 jack-up platform. More specifically, numerical calculations of structure ultimate bearing capacity have been undertaken, and the influence of model parameters on the prediction accuracy of the failure state is discussed. The results show that implicit performance function can be constructed accurately using SAE-MC method by reflecting the relationship between different critical safety state and structural vulnerability. Compared with traditional BP neural network, deep learning network has higher prediction accuracy to failure probability. The dynamic risk grade in the process of preload operation can be determined quantitatively using the reliability analysis method mentioned in this paper.