Feng Ye
Jianghan University
7 Papers
17 Citations
Feng Ye is an academic researcher from Jianghan University. The author has contributed to research in topics: Hydrogen & Adsorption. The author has an hindex of 3, co-authored 3 publications. Previous affiliations of Feng Ye include Wuhan University of Technology.
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Papers
Artificial neural network based optimization for hydrogen purification performance of pressure swing adsorption
Feng Ye,Shuo Ma,Shuo Ma,Liang Tong,Liang Tong,Jinsheng Xiao,Jinsheng Xiao,Pierre Bénard,Richard Chahine +8 more
TL;DR: In this article, an artificial neural network (ANN) model is built for predicting PSA system performance and further optimizing the operation parameters of the PSA cycle, and an optimization algorithm based on the ANN model which was trained on the data produced from Aspen model is used to train ANN model.
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Implementation for Model of Adsoptive Hydrogen Storage Using UDF in Fluent
TL;DR: In this paper, an axisymmetrical geometry model is proposed to simulate the charging, domancy, discharging and domancy processes of hydrogen storage tank based on activated carbon bed in a steel container at room temperature (302K) and medium storage pressure (10 MPa).
21
Lumped Parameter Modeling of SAE J2601 Hydrogen Fueling Tests
TL;DR: In this paper , a mathematical model for a compressed hydrogen storage tank is established based on the mass conservation equation, the energy conservation equation and the real gas equation of state, which divides the tank into a hydrogen gas zone and a tank wall zone.
Heat Transfer Analysis Methodology for Compression Hydrogen Storage Tank during Charge–Discharge Cycle
Hao Luo,Chengqing Yuan,Li Wang,Tianqi Yang,Liang Tong,Feng Ye,Yupeng Yuan,Pierre Bénard,Richard Chahine,Jin-Kun Xiao +9 more
Hydrogen purification layered bed optimization based on artificial neural network prediction of breakthrough curves
Shuo Ma,Shuo Ma,Shuo Ma,Liang Tong,Liang Tong,Feng Ye,Jinsheng Xiao,Jinsheng Xiao,Pierre Bénard,Richard Chahine +9 more
TL;DR: It is shown that the PSA cycle's optimal operation parameters can be obtained by use of ANN model and optimization algorithm, the ANN model has been trained according to the data generated by Aspen adsorption model.