| 1 |
A supervised machine learning approach for predicting variable drag forces on spherical particles in suspension
He L, Tafti DK
Powder Technology, 345, 379, 2019 |
| 2 |
Analysis of cold compaction for Fe-C, Fe-C-Cu powder design based on constitutive relation and artificial neural networks
Shin D, Lee CH, Kim SH, Park DY, Oh JW, Gal CW, Koo JM, Park SJ, Lee SC
Powder Technology, 353, 330, 2019 |
| 3 |
Predictive single-step kinetic model of biomass devolatilization for CFD applications: A comparison study of empirical correlations (EC), artificial neural networks (ANN) and random forest (RF)
Xing JK, Wang HO, Luo K, Wang S, Bai Y, Fan JR
Renewable Energy, 136, 104, 2019 |
| 4 |
An evaluation of machine learning and artificial intelligence models for predicting the flotation behavior of fine high-ash coal
Ali D, Hayat MB, Alagha L, Molatlhegi OK
Advanced Powder Technology, 29(12), 3493, 2018 |
| 5 |
Development of SVR-based model and comparative analysis with MLR and ANN models for predicting the sorption capacity of Cr(VI)
Parveen N, Zaidi S, Danish M
Process Safety and Environmental Protection, 107, 428, 2017 |
| 6 |
Application of neural networks for evaluating energy performance certificates of residential buildings
Khayatian F, Sarto L, Dall'O' G
Energy and Buildings, 125, 45, 2016 |
| 7 |
A flexible intelligent algorithm for identification of optimum mix of demographic variables for integrated HSEE-ISO systems: The case of a gas transmission refinery
Azadeh A, Sharahi ZJ, Ashjari B, Saberi M
Journal of Loss Prevention in The Process Industries, 26(6), 1159, 2013 |
| 8 |
The use of artificial neural networks (ANN) for modeling of adsorption of Cu(II) from industrial leachate by pumice
Turan NG, Mesci B, Ozgonenel O
Chemical Engineering Journal, 171(3), 1091, 2011 |
| 9 |
Artificial neural network (ANN) approach for modeling Zn(II) adsorption from leachate using a new biosorbent
Turan NG, Mesci B, Ozgonenel O
Chemical Engineering Journal, 173(1), 98, 2011 |
| 10 |
Investigation of thermal stratification in cisterns using analytical and Artificial Neural Networks methods
Siahoui HRA, Dehghani AR, Razavi M, Khani MR
Energy Conversion and Management, 52(1), 505, 2011 |
