| 1 |
Adaptive Soft Sensor Modeling Method for Time-varying and Multi-Dimensional Chemical Processes
Li LH, Dai YS
Journal of Chemical Engineering of Japan, 54(2), 63, 2021 |
| 2 |
Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons
Pang ZH, Niu FX, O'Neill Z
Renewable Energy, 156, 279, 2020 |
| 3 |
Operating performance assessment and non-optimal cause identification for chemical process
Tao Y, Shi HB, Song B, Tan S
Canadian Journal of Chemical Engineering, 97, 1475, 2019 |
| 4 |
Degradation prediction of proton exchange membrane fuel cell based on grey neural network model and particle swarm optimization
Chen K, Laghrouche S, Djerdir A
Energy Conversion and Management, 195, 810, 2019 |
| 5 |
A combined monitoring scheme with fuzzy logic filter for plant-wide Tennessee Eastman Process fault detection
Ammiche M, Kouadri A, Bakdi A
Chemical Engineering Science, 187, 269, 2018 |
| 6 |
Short term predictions of occupancy in commercial buildings-Performance analysis for stochastic models and machine learning approaches
Li ZX, Dong B
Energy and Buildings, 158, 268, 2018 |
| 7 |
Development of robust extended Kalman filter and moving window estimator for simultaneous state and parameter/disturbance estimation
Valluru J, Patwardhan SC, Biegler LT
Journal of Process Control, 69, 158, 2018 |
| 8 |
Adaptive soft sensor based on time difference Gaussian process regression with local time-delay reconstruction
Xiong WL, Li YJ, Zhao YJ, Huang B
Chemical Engineering Research & Design, 117, 670, 2017 |
| 9 |
Degradation prediction of PEM fuel cell using a moving window based hybrid prognostic approach
Zhou DM, Gao F, Breaz E, Ravey A, Miraoui A
Energy, 138, 1175, 2017 |
| 10 |
Fault detection using multiscale PCA-based moving window GLRT
Sheriff MZ, Mansouri M, Karim MN, Nounou H, Nounou M
Journal of Process Control, 54, 47, 2017 |
