Increase MPC project efficiency

Hydrocarbon Processing

Volumn 80, Issue 3, 2001, Pages 49-51+54+56

  Butoyi, F ^a   Zhu, Y ^a  

^a DOW BENELUX B V   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

EFFICIENCY; ERRORS; FREQUENCY RESPONSE; MATHEMATICAL MODELS; MATRIX ALGEBRA; MAXIMUM LIKELIHOOD ESTIMATION; MULTIVARIABLE CONTROL SYSTEMS; PARAMETER ESTIMATION; POLYNOMIALS; PREDICTIVE CONTROL SYSTEMS; TRANSFER FUNCTIONS;

ASYMPTOTIC IDENTIFICATION METHOD; CLOSED-LOOP IDENTIFICATION; DEETHANIZER; MODEL PREDICTIVE CONTROL;

PETROLEUM ENGINEERING;

EID: 0035274854     PISSN: 00188190     EISSN: None     Source Type: Trade Journal    
DOI: None     Document Type: Article

References (19)

1. Multivariable process identification for MPC: The asymptotic method and its applications
2. For model-based control design, closed-loop identification gives better performance
3. Design of identification experiments for robust control. A geometric approach for bivariate processes
4. Identification for control of strongly interactive plants
5. Identification of processes in closed loop-identifiability and accuracy aspects
6. Use of error criteria in identification for control
7. Parametric versus nonparametric models in industrial process identification for MPC
8. Asymptotic variance expressions for identified black-box transfer function models
9. A simple closed-loop test design formula for internal model control
10. Tai-Ji ID: Automatic system identification package for model based process control
11. Application of a large predictive multivariable controller to a hydrocracker second stage reactor
12. On consistency of subspace system identification methods
13. Canonical variable analysis in identification, filtering and adaptive control
14. An overview of industrial model predictive control technology
15. Industrial applications of model based predictive control
16. L2 model reduction and variance reduction
17. Closed-loop issues in system identification
18. Model reduction of high-order estimated models: The asymptotic ML approach