| 1 |
Power-draw prediction by random forest based on operating parameters for an industrial ball mill
Tohry A, Chelgani SC, Matin SS, Noormohammadi M
Advanced Powder Technology, 31(3), 967, 2020 |
| 2 |
Ensemble pattern trees for predicting hot metal temperature in blast furnace
Zhang XM, Kano M, Matsuzaki S
Computers & Chemical Engineering, 121, 442, 2019 |
| 3 |
Applying multivariate analysis for optimising the electrodialytic removal of Cu and Pb from shooting range soils
Pedersen KB, Jensen PE, Ottosen LM, Barlindhaug J
Journal of Hazardous Materials, 368, 869, 2019 |
| 4 |
Energy consumption model with energy use factors of tenants in commercial buildings using Gaussian process regression
Yoon YR, Moon HJ
Energy and Buildings, 168, 215, 2018 |
| 5 |
Pressure drop in pipe flow of cemented paste backfill: Experimental and modeling study
Qi CC, Chen QS, Fourie A, Zhao JW, Zhang QL
Powder Technology, 333, 9, 2018 |
| 6 |
Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests
Ma J, Cheng JCP
Applied Energy, 183, 193, 2016 |
| 7 |
Relative importance of factors influencing building energy in urban environment
Tian W, Liu YL, Heo YS, Yan D, Li ZY, An JJ, Yang S
Energy, 111, 237, 2016 |
| 8 |
Assessing the potential of random forest method for estimating solar radiation using air pollution index
Sun HW, Gui DW, Yan BW, Liu Y, Liao WH, Zhu Y, Lu CW, Zhao N
Energy Conversion and Management, 119, 121, 2016 |
| 9 |
Explaining relationships between coke quality index and coal properties by Random Forest method
Chelgani SC, Matin SS, Hower JC
Fuel, 182, 754, 2016 |
| 10 |
Explaining relationships among various coal analyses with coal grindability index by Random Forest
Matin SS, Hower JC, Farahzadi L, Chelgani SC
International Journal of Mineral Processing, 155, 140, 2016 |
