| 1 |
Development and analyses of data-driven models for predicting the bed depth profile of solids flowing in a rotary kiln
Parveen N, Zaidi S, Danish M
Advanced Powder Technology, 31(2), 678, 2020 |
| 2 |
From dynamic response surface models to the identification of the reaction stoichiometry in a complex pharmaceutical case study
Santos-Marques J, Georgakis C, Mustakis J, Hawkins JM
AIChE Journal, 65(4), 1173, 2019 |
| 3 |
Optimal graph Laplacian
Sato K
Automatica, 103, 374, 2019 |
| 4 |
Data-Driven Subgrid-Scale Modeling for Convection-Dominated Concentration Boundary Layers
Weiner A, Hillenbrand D, Marschall H, Bothe D
Chemical Engineering & Technology, 42(7), 1349, 2019 |
| 5 |
Estimating remaining driving range of battery electric vehicles based on real-world data: A case study of Beijing, China
Bi J, Wang YX, Sai QY, Ding C
Energy, 169, 833, 2019 |
| 6 |
Big data approach to batch process monitoring: Simultaneous fault detection and diagnosis using nonlinear support vector machine-based feature selection
Onel M, Kieslich CA, Guzman YA, Floudas CA, Pistikopoulos EN
Computers & Chemical Engineering, 115, 46, 2018 |
| 7 |
Big data approach to batch process monitoring: Simultaneous fault detection and diagnosis using nonlinear support vector machine-based feature selection(Reprinted from Computers and Chemical Engineering, vol 115, pg 46-63, 2018)
Onel M, Kieslich CA, Guzman YA, Floudas CA, Pistikopoulos EN
Computers & Chemical Engineering, 116, 503, 2018 |
| 8 |
Evaluation of data-driven models for predicting solar photovoltaics power output
Moslehi S, Reddy TA, Katipamula S
Energy, 142, 1057, 2018 |
| 9 |
Performance forecasting for polymer flooding in heavy oil reservoirs
Amirian E, Dejam M, Chen ZX
Fuel, 216, 83, 2018 |
| 10 |
Nonlinear Processes Fault Identification with Application to PCFBP
Zhai LR, Zhang YW, Zhang YZ, Fang Z, Xie Y
Journal of Chemical Engineering of Japan, 51(1), 53, 2018 |
