China Three Gorges University

About: China Three Gorges University is a education organization based out in Yichang, China. It is known for research contribution in the topics: Catalysis & Landslide. The organization has 11161 authors who have published 8011 publications receiving 82224 citations. The organization is also known as: Sanxia Daxue.

Comprehensive Design of the High-Sulfur-Loading Li–S Battery Based on MXene Nanosheets

Abstract: The lithium-sulfur battery is the subject of much recent attention due to the high theoretical energy density, but practical applications are challenged by fast decay owing to polysulfide shuttle and electrode architecture degradation. A comprehensive study of the sulfur host microstructure design and the cell architecture construction based on the MXene phase (Ti3C2Tx nanosheets) is performed, aiming at realize stable cycling performance of Li–S battery with high sulfur areal loading. The interwoven KB@Ti3C2Tx composite formed by self-assembly of MXene and Ktejen black, not only provides superior conductivity and maintains the electrode integrality bearing the volume expansion/shrinkage when used as the sulfur host, but also functions as an interlayer on separator to further retard the polysulfide cross-diffusion that possibly escaped from the cathode. The KB@Ti3C2Tx interlayer is only 0.28 mg cm−2 in areal loading and 3 μm in thickness, which accounts a little contribution to the thick sulfur electrode; thus, the impacts on the energy density is minimal. By coupling the robust KB@Ti3C2Tx cathode and the effective KB@Ti3C2Tx modified separator, a stable Li–S battery with high sulfur areal loading (5.6 mg cm−2) and high areal capacity (6.4 mAh cm−2) at relatively lean electrolyte is achieved.

Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization

Abstract: A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions.

Finite-time parameter identification and adaptive synchronization between two chaotic neural networks

Abstract: This work presents an approach for finite-time synchronization to identify all the unknown parameters for two coupled neural networks with time delay. Based on the finite-time stability theory, an effective feedback control with an updated law is designed to finite-time synchronization between two chaotic neural networks. Since finite-time topology identification means the suboptimum in the identification time, the results of this paper are important. Finally, an illustrative example is given to show the effectiveness of the main results.