Mechanism and process for the extraction of lithium from the high magnesium brine with N,N-bis(2-ethylhexyl)-2-methoxyacetamide in kerosene and FeCl3

Lianmin Ji,Licheng Zhang,Dong Shi,Xiaowu Peng,Jinfeng Li,Yuze zhang,Taoshan Xu,Lijuan Liao 한국공업화학회 2022 Journal of Industrial and Engineering Chemistry Vol.113 No.-

The degradation of TBP has become a stumbling block to the industrial continuous production of lithiumchloride extraction from brines with high Mg/Li ratio by solvent extraction. To develop a more stable andmore efficient extraction system, a novel amide system, containing newly synthesized extractant N,N-bis(2-ethylhexyl)-2-methoxyacetamide (NBEHMOA), FeCl3 6H2O and sulfonated kerosene, was proposed inthis work. Compared with TBP and N523, NBEHMOA extracted Li+ and H+ in the sequence of TBP ˃NBEHMOA ˃ N523 and N523 ˃ TBP ˃ NBEHMOA respectively. The solution containing 5.5 mol/L LiCland 0.5 mol/L HCl was used as the eluent in the scrubbing process. A Li+ extraction efficiency of 96.7%and 22.31 g/L Li+ in the stripping solution were achieved by the whole process with counter-currenttwelve stages. The extraction mechanism investigated via FT-IR spectroscopy illustrated that the metalions Fe3+ and Li+ were mainly coordinated by the carbonyl C = O in NBEHMOA. This novel amide systemcan successfully achieve the cascade extraction effect for lithium, Li/Mg separation and avoid the generationof phase interface objects through counter-current extraction of lithium from brine at a lower acidity. This work provided a novel extraction system to recover lithium from the higher magnesium/lithiumratio brines.

Preparation of layered double hydroxides intercalated by tartaric acid anion and its application in boron adsorption

Lianmin Ji,Liang Cai,Yuze zhang,Xiaowu Peng,Dong Shi,Lijuan Li 한국공업화학회 2023 Journal of Industrial and Engineering Chemistry Vol.123 No.-

Based on the exchangeability of anions between the layers of magnesium-based LDHs, LDHs intercalatedby tartaric acid anion (TA-LDH) was prepared by co-precipitation method to adsorb boron, and confirmedby XRD and FT-IR that TA had successfully entered the interlayer of LDH. The adsorption kinetics showedthat the adsorption efficiency of B was mainly controlled by the chemisorption mechanism. The thermodynamicsrevealed that the relationship between the equilibrium concentration of B in solution and theadsorption capacity of TA-LDH for B was consistent with the Freundlich model. Cl or Li+ in solution hadlittle effect on the adsorption of B by TA-LDH, while SO4 2 had a great effect on that compared with NO3-LDH. Boron adsorption was accomplished through the interaction between the hydroxyl of TA anion inthe interlayer and that of B(OH)3 and B(OH)4. The elution efficiency of TA-LDH was greater than 70% withammonium chloride used as the eluent. While TA-LDH still maintained a good adsorption capacity after 5cycles, and the solubility loss efficiencies of Mg2+ in a single adsorption and elution were 1.5% and 5.4%,respectively. Our study can provide a new idea and method for the development and utilization of magnesiumresources.

A new bismuth vanadate photocatalyst of Bi₂₃V₄O_44.5 nanoplates with layered δ-Bi₂O₃ based fluorite-type superstructures

Lu, Yuting,Chen, Luyang,Li, Yuze,Xie, Hongde,Cheng, Han,Jin Seo, Hyo Elsevier 2016 Materials letters Vol.164 No.-

<P><B>Abstract</B></P> <P>The work reports the large-scale synthesis and photocatalytic activity of Bi<SUB>23</SUB>V<SUB>4</SUB>O<SUB>44.5</SUB> nanoplates. The structural refinement was conducted in the layered δ-Bi<SUB>2</SUB>O<SUB>3</SUB> based superstructuLre. The detailed surface properties were characterized. Bi<SUB>23</SUB>V<SUB>4</SUB>O<SUB>44.5</SUB> shows efficient absorption in visible region with a band-gap of 2.384eV from the hybridization of Bi-6s and O-2p orbitals. Particularly, Bi<SUB>23</SUB>V<SUB>4</SUB>O<SUB>44.5</SUB> shows an efficient photodegradation for methylene blue (MB). The photocatalysis benefits from the layered B–O structure characteristic and the codoped defects of V<SUP>4+</SUP>/V<SUP>5+</SUP> ions in the lattices. Bi<SUB>23</SUB>V<SUB>4</SUB>O<SUB>44.5</SUB> nanoplates could be a potential photocatalyst.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Bi<SUB>23</SUB>V<SUB>4</SUB>O<SUB>44.5</SUB> nanoplates were prepared by the Pechini method. </LI> <LI> It has a narrow band-gap of 2.384eV from hybridization of Bi-6s and O-2p. </LI> <LI> Bi<SUB>23</SUB>V<SUB>4</SUB>O<SUB>44.5</SUB> has photocatalysis report visible-light-irradiation. </LI> <LI> Photocatalysis is due to layered δ-Bi<SUB>2</SUB>O<SUB>3</SUB>-like structure and codoped V<SUP>4+</SUP>/V<SUP>5+</SUP> ions. </LI> </UL> </P>

A Self-Supervised Learning Framework for Under-Sampling Pattern Design Using Graph Convolution Network

Yuze Li,Huijun Chen 대한자기공명의과학회 2020 Investigative Magnetic Resonance Imaging Vol.24 No.4

Purpose: To generate the under-sampling pattern using a self-supervised learning framework based on a graph convolutional network. Materials and Methods: We first decoded the k-space data into the graph and put it into the network. After the processing of graph convolution layers and graph pooling layers, the network generated the under-sampling pattern for MR reconstruction. We trained the network on the simulated brain dataset enabled by the selfsupervised learning strategy. We did simulation along with the in vivo brain and liver experiments under different noise levels and accelerating factors to compare the performance between the proposed method and traditional methods using the PSNR and SSIM index. Results: The simulation experiments showed that the proposed method can achieve the best performance with low accelerating factors (2 and 3) at all noise levels and in high accelerating factors (4 and 5) at high noise levels (50 and 70 dB). In in vivo experiments, the proposed method attained the highest PSNR and SSIM in the brain dataset as well as in the liver dataset after fine tuning on a small liver dataset. Conclusion: The self-supervised learning framework based on a graph convolutional network was able to design the under-sampling mask for MR reconstruction. The superior performance in the simulation and in vivo experiments demonstrated the feasibility and flexibility of the proposed method and its potential in clinical use.

Hydrothermal synthesis, characterization, and luminescence of Ca2B2O5:RE (RE = Eu3+, Tb3+, Dy3+) nanofibers

Yang, Li,Wan, Yingpeng,Li, Yuze,Pu, Yinfu,Huang, Yanlin,Chen, Cuili,Seo, Hyo Jin Springer-Verlag 2016 JOURNAL OF NANOPARTICLE RESEARCH Vol.18 No.4

<P>Ca2B2O5:RE (RE = Eu3+, Tb3+, Dy3+) nanofibers were synthesized by the hydrothermal reaction method. The structural refinement was conducted on the base of the X-ray powder diffraction (XRD) measurements. The surface properties of the Ca2B2O5:RE (RE = Eu3+, Tb3+, Dy3+) nanofibers were investigated by the measurements such as the scanning electron microscope (SEM), transmission electron microscope (TEM), and the energy dispersive spectrum (EDS). The nanofiber has a diameter of about 100 nm and a length of several micrometers. The luminescence properties such as photoluminescence excitation (PLE) and emission spectra (PL), decay lifetime, color coordinates, and the absolute internal quantum efficiency (QE) were reported. Ca2B2O5:Eu3+ nanofibers show the red luminescence with CIE coordinates of (x = 0.41, y = 0.51) and the luminescence lifetime of 0.63 ms. The luminescence of Ca2B2O5:Tb3+ nanofibers is green color (x = 0.29, y = 0.53) with the lifetime of 2.13 ms. However, Dy3+-doped Ca2B2O5 nanofibers present a singlephase white-color phosphor with the fluorescence decay of 3.05 ms. Upon near-UV excitation, the absolute quantum efficiency is measured to be 65, 35, and 37 % for Eu3+-, Tb3+-, Dy3+-doped Ca2B2O5 nanofibers, respectively. It is suggested that Ca2B2O5:RE (RE = Eu3+, Tb3+, Dy3+) nanofibers could be an efficient phosphor for lighting and display.</P>

Cognitive Virtual Network Embedding Algorithm Based on Weighted Relative Entropy

( Yuze Su ),( Xiangru Meng ),( Zhiyuan Zhao ),( Zhentao Li ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.4

Current Internet is designed by lots of service providers with different objects and policies which make the direct deployment of radically new architecture and protocols on Internet nearly impossible without reaching a consensus among almost all of them. Network virtualization is proposed to fend off this ossification of Internet architecture and add diversity to the future Internet. As an important part of network virtualization, virtual network embedding (VNE) problem has received more and more attention. In order to solve the problems of large embedding cost, low acceptance ratio (AR) and environmental adaptability in VNE algorithms, cognitive method is introduced to improve the adaptability to the changing environment and a cognitive virtual network embedding algorithmbased on weighted relative entropy (WRE-CVNE) is proposed in this paper. At first, the weighted relative entropy (WRE) method is proposed to select the suitable substrate nodes and paths in VNE. In WRE method, the ranking indicators and their weighting coefficients are selected to calculate the node importance and path importance. It is the basic of the WRE-CVNE. In virtual node embedding stage, the WRE method and breadth first search (BFS) algorithm are both used, and the node proximity is introduced into substrate node ranking to achieve the joint topology awareness. Finally, in virtual link embedding stage, the CPU resource balance degree, bandwidth resource balance degree and path hop counts are taken into account. The path importance is calculated based on the WRE method and the suitable substrate path is selected to reduce the resource fragmentation. Simulation results show that the proposed algorithm can significantly improve AR and the long-term average revenue to cost ratio (LTAR/CR) by adjusting the weighting coefficients in VNE stage according to the network environment. We also analyze the impact of weighting coefficient on the performance of the WRE-CVNE. In addition, the adaptability of the WRE-CVNE is researched in three different scenarios and the effectiveness and efficiency of the WRE-CVNE are demonstrated.

Adaptive robust target tracking control of marching tank under high-speed maneuvering condition

Cong Li,Guolai Yang,Xiuye Wang,Yuze Ma,Liqun Wang,Quanzhao Sun 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.6

This paper focuses on the problem of target tracking control of marching tanks under the condition of high-speed maneuver based on sliding mode disturbance observer. First, the dynamic model of marching tank is established by using multi-body dynamics software. The nonlinear factors such as barrel flexibility, clearance between barrel and bushing, and uncertainty sources such as road excitation are considered. Second, the mechatronics model of tank bidirectional stabilization system considering transmission clearance and friction is established in numerical simulation software. Aiming at the tank bidirectional stabilization system, an adaptive robust controller based on sliding mode disturbance observer is designed. Finally, the real-time control and target tracking of the tank bidirectional stabilization system are realized by co-simulation. The results show that the sliding mode disturbance observer improves the stability accuracy of the system by 43.22 %. Comparing the simulation results with the PID control, the controller designed in this paper has the characteristics of rapid tracking speed and high robustness, and the advantage is more obvious under the condition of highspeed maneuver.

Learning Multiple-Gait Quadrupedal Locomotion via Hierarchical Reinforcement Learning

Lang Wei,Yunxiang Li,Yunfei Ai,Yuze Wu,Hao Xu,Wei Wang,Guoming Hu 한국정밀공학회 2023 International Journal of Precision Engineering and Vol.24 No.9

Over long periods of evolution, legged animals have developed the capability to use a variety of gaits to move efficiently and flexibly at different speeds. To enable quadruped robots to acquire this ability, this study proposes a two-stage training hierarchical framework that can have quadruped robots generate energy-efficient multiple-gait locomotion, consisting of a gait selection policy module and a react controller module. The parameters of both modules are optimized using reinforcement learning. The experimental results in the simulation demonstrate that the proposed method can generate energy-efficient multiple-gait quadrupedal locomotion compared to previous methods. To validate the robustness and effectiveness of the method, we constructed a closed-chain quadruped robot and deployed the controller trained by the method to the robot. The experimental results in the real world suggest that the controller can enable the robot to move stably and efficiently in different gaits. The main contribution of this paper is that the authors propose a novel hierarchical framework, which makes quadruped robots use an optimal gait at a specific speed and smoothly switch to another one after getting a different speed command. These behaviors are automatically produced through simulation training, eliminating the need for the tedious work of designing gaits and modulating controllers. Experimental results showcase that the proposed method has significant advantages compared to previous methods.

Virtual Network Embedding based on Node Connectivity Awareness and Path Integration Evaluation

( Zhiyuan Zhao ),( Xiangru Meng ),( Yuze Su ),( Zhentao Li ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.7

As a main challenge in network virtualization, virtual network embedding problem is increasingly important and heuristic algorithms are of great interest. Aiming at the problems of poor correlation in node embedding and link embedding, long distance between adjacent virtual nodes and imbalance resource consumption of network components during embedding, we herein propose a two-stage virtual network embedding algorithm NA-PVNM. In node embedding stage, resource requirement and breadth first search algorithm are introduced to sort virtual nodes, and a node fitness function is developed to find the best substrate node. In link embedding stage, a path fitness function is developed to find the best path in which available bandwidth, CPU and path length are considered. Simulation results showed that the proposed algorithm could shorten link embedding distance, increase the acceptance ratio and revenue to cost ratio compared to previously reported algorithms. We also analyzed the impact of position constraint and substrate network attribute on algorithm performance, as well as the utilization of the substrate network resources during embedding via simulation. The results showed that, under the constraint of substrate resource distribution and virtual network requests, the critical factor of improving success ratio is to reduce resource consumption during embedding.