Yanshan University

About: Yanshan University is a education organization based out in Qinhuangdao, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 19544 authors who have published 16904 publications receiving 184378 citations. The organization is also known as: Yānshān dàxué.

Facile Preparation of Rod-like MnO Nanomixtures via Hydrothermal Approach and Highly Efficient Removal of Methylene Blue for Wastewater Treatment

Abstract: In the present study, nanoscale rod-shaped manganese oxide (MnO) mixtures were successfully prepared from graphitic carbon nitride (C3N4) and potassium permanganate (KMnO4) through a hydrothermal method. The as-prepared MnO nanomixtures exhibited high activity in the adsorption and degradation of methylene blue (MB). The as-synthesized products were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), surface area analysis, X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). Furthermore, the effects of the dose of MnO nanomixtures, pH of the solution, initial concentration of MB, and the temperature of MB removal in dye adsorption and degradation experiments was investigated. The degradation mechanism of MB upon treatment with MnO nanomixtures and H2O2 was studied and discussed. The results showed that a maximum adsorption capacity of 154 mg g−1 was obtained for a 60 mg L−1 MB solution at pH 9.0 and 25 °C, and the highest MB degradation ratio reached 99.8% under the following optimum conditions: 50 mL of MB solution (20 mg L−1) at room temperature and pH ≈ 8.0 with 7 mg of C, N-doped MnO and 0.5 mL of H2O2.

Hybrid genetic algorithm-based optimization of powertrain and control parameters of plug-in hybrid electric bus

Abstract: This paper proposes a novel hybrid genetic algorithm for the simultaneous optimization of the powertrain and control parameters in plug-in hybrid electric bus (PHEB) with trade-off between economy and dynamic performance. PHEBs are potential public transportations to alleviate energy shortages and urban environment pollution. The PHEB powertrain and control parameters significantly impact the vehicle performance and economy, and an optimization process is needed to design a set of optimized parameters for a given driving route. A novel hybrid genetic algorithm (HGA) which combines an enhanced genetic algorithm (EGA) with simulated annealing (SA) is proposed in this paper. By merging EGA with SA, simulated annealing process is applied to the better half population after EGA operations, and then an adaptive cooling schedule is introduced. In addition, several techniques are implemented to achieve the goals of sustaining the convergence capacity and maintaining diversity in the population, such as orthogonal design method, adaptive mechanisms of crossover and mutation probabilities. A solution relative error distance is defined to express the performance of standard genetic algorithm (SGA), EGA, and HGA. The optimization is performed over the following two driving cycles: (1) a driving cycle CYC_873 collected from a real bus route; and (2) Urban Dynamometer Driving Schedule+China Typical Urban Driving Cycle (UDDS+CTUDC). Simulation results indicate that the convergence speed and global searching ability of HGA are significantly better for optimal PHEB powertrain and control parameters design. And the optimal parameters might obtain the best comprehensive performance of PHEB for the given Chinese urban driving cycles.