| 1 |
Rebooting data-driven soft-sensors in process industries: A review of kernel methods
Liu YQ, Xie M
Journal of Process Control, 89, 58, 2020 |
| 2 |
Soft sensor design for variable time delay and variable sampling time
Griesing-Scheiwe F, Shardt YAW, Perez-Zuniga G, Yang X
Journal of Process Control, 92, 310, 2020 |
| 3 |
Optimal Selection of Time Resolution for Batch Data Analysis. Part I: Predictive Modeling
Rato TJ, Reis MS
AIChE Journal, 64(11), 3923, 2018 |
| 4 |
Modelling the strip thickness in hot steel rolling mills using least-squares support vector machines
Shardt YAW, Mehrkanoon S, Zhang K, Yang X, Suykens J, Ding SX, Peng KX
Canadian Journal of Chemical Engineering, 96(1), 171, 2018 |
| 5 |
Soft sensors model optimization and application for the refinery real-time prediction of toluene content
Mohler I, Andrijic ZU, Bolf N
Chemical Engineering Communications, 205(3), 411, 2018 |
| 6 |
Data-driven soft-sensors for online monitoring of batch processes with different initial conditions
Shokry A, Vicente P, Escudero G, Perez-Moya M, Graells M, Espuna A
Computers & Chemical Engineering, 118, 159, 2018 |
| 7 |
Predicting the unpredictable: Consideration of human and organisational factors in maintenance prognostics
McDonnell D, Balfe N, Pratto L, O'Donnell GE
Journal of Loss Prevention in The Process Industries, 54, 131, 2018 |
| 8 |
Robust probabilistic principal component analysis based process modeling: Dealing with simultaneous contamination of both input and output data
Sadeghian A, Wu O, Huang B
Journal of Process Control, 67, 94, 2018 |
| 9 |
Adaptive just-in-time and relevant vector machine based soft-sensors with adaptive differential evolution algorithms for parameter optimization
Liu YQ
Chemical Engineering Science, 172, 571, 2017 |
| 10 |
Multiple adaptive mechanisms for data-driven soft sensors
Bakirov R, Gabrys B, Fay D
Computers & Chemical Engineering, 96, 42, 2017 |
