Yuan Li

Kunming University of Science and Technology

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Kai Li

Kunming University of Science and Technology

Fei Wang

Kunming University of Science and Technology

Guanbin Gao

Kunming University of Science and Technology

Xin Sun

Kunming University of Science and Technology

Ping Ning

Kunming University of Science and Technology

Yuyi Yang

Chinese Academy of Sciences

Ping Ning

Kunming University of Science and Technology

Chi Wang

Shanghai University

Ping Ning

Kunming University of Science and Technology

Youquan Liu

ShanghaiTech University

Cooperative Institutions

Kunming University of Science and Technology

Chinese Academy of Sciences

Kunming University

University of Chinese Academy of Sciences

Jilin University

Shanghai Jiao Tong University

Beijing Institute of Technology

Tsinghua University

China University of Mining and Technology

National Engineering Research Center for Wheat

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"Reduction-Aggregation" Strategy to Construct a Low-Cost and High-Efficiency Ag/Al2o3 Catalyst for Nh3-Sco

Zhao Li Jie Zhao Jing Gao Yuan Li Shuangyou Bao

Ammonia (NH3) is the main alkaline gas in the atmosphere and neutralizes acidic gases to form haze. Silver-based catalysts are widely used in the field of ammonia selective catalytic oxidation (NH3-SCO) due to their low cost and high efficiency. However, Ag-based catalysts with high activity at low temperatures are usually loaded with large amounts of Ag. To further reduce the cost of Ag-based catalysts, we used a “reduction-aggregation” strategy to aggregate isolated Ag atoms that were highly dispersed into Ag nanoparticles (AgNPs) so that more active centers could be exposed under low Ag loading conditions. Significant AgNPs size effects in the NH3-SCO reaction were found, including the effect of AgNPs size on catalyst activity and stability. Finally, novel Ag/Al2O3 catalysts with high efficiency and stability were developed at a low Ag loading of 1 wt.% and achieved better NH3-SCO performance than 10% Ag/Al2O3, which significantly reduced the catalyst cost.

Cost reduction

10.2139/ssrn.4374678

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A Machine Learning-Based QSAR Model for Benzimidazole Derivatives as Corrosion Inhibitors by Incorporating Comprehensive Feature Selection

Interdisciplinary Sciences Computational Life Sciences (2019)

Youquan Liu Yanzhi Guo Wengang Wu Xiong Ying Chuan Sun

Molecular descriptor

Feature (linguistics)

Benzimidazole

10.1007/s12539-019-00346-7

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Citations (40)

Design of a Measurement System for Industrial Robots' Calibration Based on Cable Encoders

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) (2019)

Yuan Li Guanbin Gao Jing Na Hongwei Zhang

To acquire the non-complete end information of industrial robots, a novel measurement system based on single-station cable actuated position encoder was designed. Firstly, the overall technical scheme of the measurement system was developed. Then the components of the system were selected based on the design principle, and the mechanical structure of the measurement system was designed. Based on C#, the upper computer software for acquiring the data of the encoder was developed. Based on Visual C, we developed the communication software which acquires the joint angles of industrial robots. With the joint angles, the coordinates of the industrial robot's end were calculated through the kinematic model based on MDH method. The experimental results show that the system can complete the measurement work in the non-complete end information calibration system of industrial robots.

Rotary encoder

10.1109/ddcls.2019.8908953

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Theoretical analysis of selective catalytic oxidation of H2S on Fe-N3 co-doped graphene

Molecular Catalysis (2022)

Yuan Li Yuyi Yang Kai Li Xin Sun Fei Wang

Electron acceptor

10.1016/j.mcat.2022.112318

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Citations (11)

Urea-Modified Cu-Based Materials: Highly Efficient and Support-Free Adsorbents for Low-Temperature Removal of H2s in an Anaerobic and Dry Environment

SSRN Electronic Journal (2022)

Jiayu Feng Lijuan Jia Fei Wang Xin Sun Ping Ning

In this work, we report a novel urea-modified copper-based material (XUrea@Cu) obtained by directly calcinating a mixture of urea and copper nitrate. This material enables efficient purification of H2S under low temperature (100 °C), dry, and anaerobic conditions. The performance test results showed that the H2S capacity of the optimal adsorbent (2Urea@Cu) could reach 364.2 mg(H2S)∙gsorbent-1, which is much superior to those of other adsorbents in the literature. The further study demonstrates that the ultra-desulfurization performance of 2Urea@Cu is mainly attributed to the high specific surface area, the extensive basic sites, the high concentrations of active oxygen, and the abundance of oxygen vacancy; which are quite effective for capturing H2S (acidic and strong reducibility). Interestingly, the deactivation adsorbent (De-2Urea@Cu) is mainly composed of high-purity CuS, which means that the Urea@Cu adsorbent can simultaneously achieve efficient purification of H2S and recovery of sulfur resources since CuS is a valuable product. In addition, in-situ FT-IR and theoretical calculation results indicate that H2S molecules dissociate on the CuO(111) surface by stepwise dehydrogenation, while S combines with adjacent Cu to form CuS. The consumption of active components (CuO) and the accumulation of reaction products (CuS) are the primary reasons for the deactivation of adsorbents. Considering the simple preparation process, low cost, and excellent H2S adsorption activity, these urea-modified copper-based materials are very promising H2S adsorbents.

10.2139/ssrn.4161665

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Identification the Effect of Coordination States of Co on Co-Nx Co-Doped Graphene for Selective Oxidation of H2s: A Dft Study

SSRN Electronic Journal (2022)

Yuan Li Xueqi Wang Yuyi Yang Juhong Zhan Ping Ning

The transition metal coordinated with nitrogen atoms doped carbon catalysts shows great potential on H2S selective oxidation into elemental sulfur. Herein, we conducted Co-N3 and Co-N4 co-doped graphene as model materials to elucidate the regulating effect of coordination environment of Co atom on the catalytic performance by Density Functional Theory (DFT) methods. The adsorption behavior, dissociation, oxidation by atomic O and O2 have been studied in detail. The Co-N3 doped graphene (PG-Co-N3 ) exhibits stronger affinity towards both H2S and O2 molecules with adsorption energy of -0.774 eV and -2.385 eV than Co-N4 doped graphene (PG-Co-N4 ) (-0.426 eV, -0.690 eV). Among three possible reaction routes, the oxidation by atomic O is the most favorable reaction pathway on both PG-Co-N3 and PG-Co-N4 surfaces. In other two routes, H2S dissociation with or without O2 assisting on PG-Co-N3 demonstrates lower reaction energy barrier of the rate determining steps than on PG-Co-N4 . These results could provide a new strategy to design novel catalysts for H2S selective oxidation, and reveal the effect of coordination environment of transition metal and nitrogen co-doped graphene on the mechanism of oxidation process.

Coordination complex

Identification

10.2139/ssrn.4235889

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Kinematic Calibration of Industrial Robots Based on Distance Information Using a Hybrid Identification Method

Complexity (2021)

Guanbin Gao Yuan Li Fei Liu Shichang Han

To improve the positioning accuracy of industrial robots and avoid using the coordinates of the end effector, a novel kinematic calibration method based on the distance information is proposed. The kinematic model of an industrial robot is established. The relationship between the moving distance of the end effector and the kinematic parameters is analyzed. Based on the results of the analysis and the kinematic model of the robot, the error model with displacements as the reference is built, which is linearized for the convenience of the following identification. The singular value decomposition (SVD) is used to eliminate the redundant parameters of the error model. To solve the problem that traditional optimization algorithms are easily affected by data noise in high dimension identification, a novel extended Kalman filter (EKF) and regularized particle filter (RPF) hybrid identification method is presented. EKF is used in the preidentification of the linearized error model. With the preidentification results as the initial parameters, RPF is used to identify the kinematic parameters of the linearized error model. Simulations are carried out to validate the effectiveness of the proposed method, which shows that the method can identify the error of the parameters and after compensation the accuracy of the robot is improved.

Robot calibration

Industrial robot

10.1155/2021/8874226

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Citations (14)

Density functional theory study of single Ni atom decorated N-doped graphene for high performance H2S sensor

Chemical Physics Letters (2021)

Yuan Li Yuyi Yang Kai Li Yingxu Wang Ping Ning

10.1016/j.cplett.2021.138338

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Citations (10)

Kinematic Parameter Identification and Error Compensation of Industrial Robots Based on Unscented Kalman Filter with Adaptive Process Noise Covariance

Machines (2024)

Guanbin Gao Xinyang Guo Gengen Li Yuan Li Houchen Zhou

Kinematic calibration plays a pivotal role in enhancing the absolute positioning accuracy of industrial robots, with parameter identification and error compensation constituting its core components. While the conventional parameter identification method, based on linearization, has shown promise, it suffers from the loss of high-order system information. To address this issue, we propose an unscented Kalman filter (UKF) with adaptive process noise covariance for robot kinematic parameter identification. The kinematic model of a typical 6-degree-of-freedom industrial robot is established. The UKF is introduced to identify the unknown constant parameters within this model. To mitigate the reliance of the UKF on the process noise covariance, an adaptive process noise covariance strategy is proposed to adjust and correct this covariance. The effectiveness of the proposed algorithm is then demonstrated through identification and error compensation experiments for the industrial robot. Results indicate its superior stability and accuracy across various initial conditions. Compared to the conventional UKF algorithm, the proposed approach enhances the robot’s accuracy stability by 25% under differing initial conditions. Moreover, compared to alternative methods such as the extended Kalman algorithm, particle swarm optimization algorithm, and grey wolf algorithm, the proposed approach yields average improvements of 4.13%, 26.47%, and 41.59%, respectively.

Identification

10.3390/machines12060406

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A Smooth Trajectory Planning for High Tibial Osteotomy Surgery with Robot-assisted Based on NURBS and S-curve Method

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) (2024)

Yuan Li Guanbin Gao Yi Bao Xinkai Chen Xinyang Guo

High tibial osteotomy

10.1109/ddcls61622.2024.10606684

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Citations (0)