| 1 |
Soft sensor development based on improved just-in-time learning and relevant vector machine for batch processes
Wang JL, Qiu KP, Guo YQ, Wang RT, Zhou XJ
Canadian Journal of Chemical Engineering, 99(1), 334, 2021 |
| 2 |
A batch-wise LSTM-encoder decoder network for batch process monitoring
Ren JY, Ni D
Chemical Engineering Research & Design, 164, 102, 2020 |
| 3 |
An integrated robust iterative learning control strategy for batch processes based on 2D system
Zhou LM, Jia L, Wang YL, Peng DG, Tan WD
Journal of Process Control, 85, 136, 2020 |
| 4 |
Robust switched predictive control for multi-phase batch processes with uncertainties and unknown disturbances
Peng B, Shi HY, Su CL, Li P
Journal of Process Control, 94, 110, 2020 |
| 5 |
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 |
| 6 |
Online prediction of quality-related variables for batch processes using a sequential phase partition method
Li Z, Wang P, Gao XJ, Qi YS, Chang P
Canadian Journal of Chemical Engineering, 97(9), 2483, 2019 |
| 7 |
Hierarchical batch-to-batch optimization of cobalt oxalate synthesis process based on data-driven model
Jia RD, Mao ZZ, He DK, Chu F
Chemical Engineering Research & Design, 144, 185, 2019 |
| 8 |
Transitional phase modeling and monitoring with respect to the effect of its neighboring phases
Gao ZP, Jia MX, Mao ZZ, Zhao LP
Chemical Engineering Research & Design, 145, 288, 2019 |
| 9 |
The design and scheduling of chemical batch processes: Computational complexity studies
de Miranda JL
Computers & Chemical Engineering, 121, 367, 2019 |
| 10 |
Model predictive control of uni-axial rotational molding process
Garg A, Gomes FPC, Mhaskar P, Thompson MR
Computers & Chemical Engineering, 121, 306, 2019 |
