| 1 |
Prediction of coal and gas outburst: A method based on the BP neural network optimized by GASA
Wu YQ, Gao RL, Yang JZ
Process Safety and Environmental Protection, 133, 64, 2020 |
| 2 |
Calibration and verification of DEM parameters for dynamic particle flow conditions using a backpropagation neural network
Ye FP, Wheeler C, Chen B, Hu JQ, Chen KK, Chen W
Advanced Powder Technology, 30(2), 292, 2019 |
| 3 |
Thermodynamic analysis and performance prediction on dynamic response characteristic of PCHE in 1000 MW S-CO2 coal fired power plant
Ma T, Li MJ, Xu JL, Cao F
Energy, 175, 123, 2019 |
| 4 |
Rutile nanopowders for pigment production: Formation mechanism and particle size prediction
Zhang W, Tang HX
Chemical Physics Letters, 692, 129, 2018 |
| 5 |
Optimal energy management strategy for a plug-in hybrid electric commercial vehicle based on velocity prediction
Shen PH, Zhao ZG, Zhan XW, Li JW, Guo QY
Energy, 155, 838, 2018 |
| 6 |
Short-term wind speed forecasting based on fast ensemble empirical mode decomposition, phase space reconstruction, sample entropy and improved back-propagation neural network
Sun W, Wang YW
Energy Conversion and Management, 157, 1, 2018 |
| 7 |
A study on energy performance of 30 commercial office buildings in Hong Kong
Jing R, Wang M, Zhang RX, Li N, Zhao YR
Energy and Buildings, 144, 117, 2017 |
| 8 |
Numerical analysis of factors influencing explosion suppression of a vacuum chamber
Shao H, Jiang SG, He XJ, Wu ZY, Zhang X, Wang K
Journal of Loss Prevention in The Process Industries, 45, 255, 2017 |
| 9 |
Comparison of Prediction Models for Power Draw in Grinding and Flotation Processes in a Gold Treatment Plant
Dong S, Wang B, Wang Z, Hu XK, Song HC, Liu Q
Journal of Chemical Engineering of Japan, 49(2), 204, 2016 |
| 10 |
Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method
Wang SX, Zhang N, Wu L, Wang YM
Renewable Energy, 94, 629, 2016 |
