| 1 |
The extraction of nickel by emulsion liquid membranes using Cyanex 301 as extractant
Ma H, Kokkilic O, Marion CM, Multani RS, Waters KE
Canadian Journal of Chemical Engineering, 96(7), 1585, 2018 |
| 2 |
Intensification of emulsion liquid membrane extraction of uranium(VI) by replacing nitric acid with sodium nitrate solution
Kulkarni SS, Juvekar VA, Mukhopadhyay S
Chemical Engineering and Processing, 125, 18, 2018 |
| 3 |
Production prediction and energy-saving model based on Extreme Learning Machine integrated ISM-AHP: Application in complex chemical processes
Geng ZQ, Li HD, Zhu QX, Han YM
Energy, 160, 898, 2018 |
| 4 |
A novel method based on extreme learning machine to predict heating and cooling load through design and structural attributes
Kumar S, Pal SK, Singh RP
Energy and Buildings, 176, 275, 2018 |
| 5 |
Solar output power forecast using an ensemble framework with neural predictors and Bayesian adaptive combination
Raza MQ, Mithulananthan N, Summerfield A
Solar Energy, 166, 226, 2018 |
| 6 |
Balancing indoor thermal comfort and energy consumption of ACMV systems via sparse swarm algorithms in optimizations
Zhai DQ, Soh YC
Energy and Buildings, 149, 1, 2017 |
| 7 |
The use of the emulsion liquid membrane technique to remove copper ions from aqueous systems using statistical experimental design
Ma H, Kokkilic O, Waters KE
Minerals Engineering, 107, 88, 2017 |
| 8 |
An ensemble prediction intervals approach for short-term PV power forecasting
Ni Q, Zhuang SX, Sheng HM, Kang GQ, Xiao J
Solar Energy, 155, 1072, 2017 |
| 9 |
A neural network approach to simulating the dynamic extraction process of L-phenylalanine from sodium chloride aqueous solutions by emulsion liquid membrane
Fang ZX, Liu XJ, Zhang MD, Sun JR, Mao SM, Lu J, Rohani S
Chemical Engineering Research & Design, 105, 188, 2016 |
| 10 |
The use of ELM-WT (extreme learning machine with wavelet transform algorithm) to predict exergetic performance of a DI diesel engine running on diesel/biodiesel blends containing polymer waste
Aghbashlo M, Shamshirband S, Tabatabaei M, Yee PL, Larimi YN
Energy, 94, 443, 2016 |
