| 1 |
Dynamic plant-wide process monitoring based on distributed slow feature analysis with inter-unit dissimilarity
Huang R, Li Z, Cao B
Korean Journal of Chemical Engineering, 39(2), 275, 2022 |
| 2 |
Development of augmented virtual reality-based operator training system for accident prevention in a refinery
Ko CJ, Lee HD, Lim YS, Lee WB
Korean Journal of Chemical Engineering, 38(8), 1566, 2021 |
| 3 |
Performance study of multi-source driving yaw system for aiding yaw control of wind turbines
Dai JC, He T, Li MM, Long X
Renewable Energy, 163, 154, 2021 |
| 4 |
Deep neural network based recursive feature learning for nonlinear dynamic process monitoring
Zhu JZ, Shi HB, Song B, Tan S, Tao Y
Canadian Journal of Chemical Engineering, 98(4), 919, 2020 |
| 5 |
Inline Spectroscopy-Based Optimization of Chemical Reactions Considering Dynamic Process Conditions
Trunina D, Liauw MA
Chemie Ingenieur Technik, 92(5), 659, 2020 |
| 6 |
Dynamic Process Operation Under Demand Response - A Review of Methods and Tools
Esche E, Repke JU
Chemie Ingenieur Technik, 92(12), 1898, 2020 |
| 7 |
A multi-feature extraction technique based on principal component analysis for nonlinear dynamic process monitoring
Guo LL, Wu P, Lou SW, Gao JF, Liu YC
Journal of Process Control, 85, 159, 2020 |
| 8 |
Multiphase two-dimensional time-slice dynamic system for batch process monitoring
Zhu JL, Yao Y, Gao FR
Journal of Process Control, 85, 184, 2020 |
| 9 |
Dynamic process monitoring based on a time-serial multi-block modeling approach
Wan XC, Tong CD, Meng SJ, Lan T
Journal of Process Control, 89, 22, 2020 |
| 10 |
A new soft-sensor algorithm with concurrent consideration of slowness and quality interpretation for dynamic chemical process
Qin Y, Zhao CH, Huang B
Chemical Engineering Science, 199, 28, 2019 |
