| 1 |
Collection of benchmark test problems for data reconciliation and gross error detection and identification
do Valle EC, Kalid RD, Secchi AR, Kiperstok A
Computers & Chemical Engineering, 111, 134, 2018 |
| 2 |
Enhanced turbine monitoring using emissions measurements and data reconciliation
Syed MS, Dooley KM, Madron F, Knopf FC
Applied Energy, 173, 355, 2016 |
| 3 |
Identification and isolability of multiple gross errors in measured data for power plants
Guo SS, Liu P, Li Z
Energy, 114, 177, 2016 |
| 4 |
Bayesian method for simultaneous gross error detection and data reconciliation
Yuan Y, Khatibisepehr S, Huang B, Li ZK
AIChE Journal, 61(10), 3232, 2015 |
| 5 |
Simultaneous data reconciliation and gross error detection for dynamic systems using particle filter and measurement test
Zhang ZJ, Chen JH
Computers & Chemical Engineering, 69, 66, 2014 |
| 6 |
On-line state estimation of nonlinear dynamic systems with gross errors
Nicholson B, Lopez-Negrete R, Biegler LT
Computers & Chemical Engineering, 70, 149, 2014 |
| 7 |
Gross error isolability for operational data in power plants
Jiang XL, Liu P, Li Z
Energy, 74, 918, 2014 |
| 8 |
Data reconciliation and gross error detection for operational data in power plants
Jiang XL, Liu P, Li Z
Energy, 75, 14, 2014 |
| 9 |
Data Reconciliation for power systems monitoring: Application to a microturbine-based test rig
Martini A, Sorce A, Traverso A, Massardo A
Applied Energy, 111, 1152, 2013 |
| 10 |
Dynamic bayesian approach to gross error detection and compensation with application toward an oil sands process
Gonzalez R, Huang B, Xu FW, Espejo A
Chemical Engineering Science, 67(1), 44, 2012 |
