Gas-liquid separator modelling and simulation with Gaussian-process models

Simulation Modelling Practice and Theory

Volumn 16, Issue 8, 2008, Pages 910-922

  Kocijan, J ^a,b   Likar, B ^c  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b UNIVERSITY OF NOVA GORICA   (Slovenia)

^c KOLEKTOR GROUP D O O   (Slovenia)

Author keywords

Dynamic system models; Gaussian process models; Simulation; System identification

Indexed keywords

CHLORINE COMPOUNDS; COMPUTATIONAL COMPLEXITY; NONLINEAR SYSTEMS; PROBABILITY DISTRIBUTIONS; SEPARATION; SEPARATORS; TRELLIS CODES;

CASE STUDIES; COMPLEX NONLINEAR SYSTEMS; COMPUTATIONAL BURDEN; DYNAMIC SYSTEM MODELS; DYNAMIC SYSTEMS; GAS-LIQUID SEPARATORS; GAUSSIAN; GAUSSIAN-PROCESS MODELS; MODEL SIMULATIONS; MODEL VALIDATIONS; MODELLING AND SIMULATION; NONPARAMETRIC MODELING; PROCESS MODELLING; SIMULATION; SYSTEM IDENTIFICATION; UNCERTAINTY PROPAGATION;

NORMAL DISTRIBUTION;

EID: 49549124024     PISSN: 1569190X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.simpat.2008.05.007     Document Type: Article

References (14)

1. Ažman K., and Kocijan J. Application of Gaussian processes for black-box modelling of biosystems. ISA Transactions 46 4 (2007) 443-457
2. Girard A., Rasmussen C.E., Quinonero Candela J., and Murray-Smith R. Gaussian process priors with uncertain inputs - application to multiple-step ahead time series forecasting. In: Becker S., Thrun S., and Obermayer K. (Eds). Advances in Neural Information Processing Systems vol. 15 (2003), MIT Press 542-552
3. Girard A., and Murray-Smith R. Gaussian process: prediction at a noisy input and application to iterative multiple-step ahead forecasting of time-series. In: Murray-Smith R., and Shorten R. (Eds). Switching and Learning in Feedback Systems (2005), Springer
4. A. Girard, Approximate methods for propagation of uncertainty with gaussian process models, Ph.D. Thesis, University of Glasgow, Glasgow, UK, 2004.
5. Gregorčič G., and Lightbody G. Gaussian process approaches to nonlinear modelling and control. In: Ruano A. (Ed). Intelligent Control Systems using Computational Intelligence Techniques. IEE Intelligent Control Series (2005), IEE
6. Gregorčič G., and Lightbody G. Local model identification with Gaussian processes. IEEE Transactions on Neural Networks 18 5 (2007) 1404-1423
7. Kocijan J., Girard A., Banko B., and Murray-Smith R. Dynamic systems identification with Gaussian processes. Mathematical and Computer Modelling of Dynamic Systems 11 (2005) 411-424
8. B. Likar, Gaussian process based nonlinear model predictive control, MSc. Thesis, University of Ljubljana, Ljubljana, 2004 (in Slovene).
9. Likar B., and Kocijan J. Predictive control of a gas-liquid separation plant based on a Gaussian process model. Computers and Chemical Engineering 31 3 (2007) 142-152
10. W.E. Leithead, Y. Zhang, K.S. Neo, Wind turbine rotor acceleration: Identification using Gaussian regression, in: Proceedings of International Conference on Informatics in Control Automation and Robotics (ICINCO), Barcelona, 2005.
11. J. Quinonero-Candela, A. Girard, J. Larsen, C.E. Rasmussen, Propagation of uncertainty in Bayesian kernel models - Application to multiple-step ahead forecasting, in: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 2, 2003, pp. 701-704.
12. Rasmussen C.E., and Williams C.K.I. Gaussian Processes for Machine Learning (2006), The MIT Press, Cambridge, MA
13. K. Thompson, D.J. Murray-Smith, Implementation of Gaussian process models for nonlinear system identification, in: I. Troch, F. Breitenecker (Eds.), Proceedings of the Fifth Vienna Symposium on Mathematical Modeling - MathMod, Vienna, 2006.
14. D. Vrančić, D. Juričić, J. Petrovčič, Measurements and mathematical modelling of a semi-industrial liquid-gas separator for the purpose of fault diagnosis. Report DP-7260, Jozef Stefan Institute, Ljubljana, 1995. .