| 1 |
Data-driven nonlinear chemical process fault diagnosis based on hierarchical representation learning
Wang Y, Jiang QC
Canadian Journal of Chemical Engineering, 98(10), 2150, 2020 |
| 2 |
An on-line framework for monitoring nonlinear processes with multiple operating modes
Tan RM, Cong T, Ottewill JR, Baranowski J, Thornhill NF
Journal of Process Control, 89, 119, 2020 |
| 3 |
Data-driven stochastic robust optimization: General computational framework and algorithm leveraging machine learning for optimization under uncertainty in the big data era
Ning C, You FQ
Computers & Chemical Engineering, 111, 115, 2018 |
| 4 |
Machine learning: Overview of the recent progresses and implications for the process systems engineering field
Lee JH, Shin J, Realff MJ
Computers & Chemical Engineering, 114, 111, 2018 |
| 5 |
Feature learning and process monitoring of injection molding using convolution-deconvolution auto encoders
Mao T, Zhang Y, Ruan YF, Gao H, Zhou HM, Li DQ
Computers & Chemical Engineering, 118, 77, 2018 |
| 6 |
Control of electrically heated floor for building load management: A simplified self-learning predictive control approach
Thieblemont H, Haghighat F, Moreau A, Lacroix G
Energy and Buildings, 172, 442, 2018 |
| 7 |
Ensemble local kernel learning for online prediction of distributed product outputs in chemical processes
Liu Y, Zhang ZJ, Chen JH
Chemical Engineering Science, 137, 140, 2015 |
| 8 |
Learning from the past and knowledge management: Are we making progress?
Pasman HJ
Journal of Loss Prevention in The Process Industries, 22(6), 672, 2009 |
| 9 |
Suboptimal mean controllers for bounded and dynamic stochastic distributions
Wang YJ, Wang H
Journal of Process Control, 12(3), 445, 2002 |
