Abstract
Gaussian process are emerging as a relatively new soft sensor building technique with promising results. This paper proposes a Gaussian Process Inferential Control System (GP-ICS) to control infrequently-measured variables in industrial processes. This is achieved by utilising an adaptive Gaussian process-based soft sensor to provide accurate reliable and continuous online predictions of difficult to measure variables and feeding them back to a PI controller. The contributions of the paper are i) the introduction of Gaussian process-based soft sensors in building inferential control systems, ii) we empirically show that the Gaussian process based inferential controller outperforms the ANN-based controller.
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References
Bahar, A., Özgen, C.: State estimation and inferential control for a reactive batch distillation column. Engineering Applications of Artificial Intelligence 23(2), 262–270 (2010)
Corchado, E., Woźniak, M., Abraham, A., de Carvalho, A.C., Snášel, V.: Recent trends in intelligent data analysis. Neurocomputing 126, 1–2 (2014)
Kadlec, P., Gabrys, B.: Adaptive local learning soft sensor for inferential control support. In: 2008 International Conference on Computational Intelligence for Modelling Control & Automation, pp. 243–248. IEEE (2008)
Geethalakshmi, S., Pappa, N.: Artificial neural network based soft sensor for fermentation of recombinant pichia pastoris. In: 2010 International Conference on Advances in Computer Engineering (ACE), pp. 148–152. IEEE (2010)
Souza, F., Santos, P., Arajo, R.: Variable and delay selection using neural networks and mutual information for data-driven soft sensors. In: 2010 IEEE Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8. IEEE (2010)
Zhang, X., Huang, W., Zhu, Y., Chen, S.: A novel soft sensor modelling method based on kernel pls. In: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS), vol. 1, pp. 295–299. IEEE (2010)
Ge, Z., Gao, F., Song, Z.: Mixture probabilistic pcr model for soft sensing of multimode processes. Chemometrics and Intelligent Laboratory Systems 105(1), 91–105 (2011)
Abusnina, A., Kudenko, D.: Adaptive soft sensor based on moving gaussian process window. In: IEEE International Conference on Industrial Technology (ICIT), pp. 1051–1056 (2013)
Rasmussen, C., Williams, C.: Gaussian processes for machine learning, vol. 38, pp. 715–719. The MIT Press, Cambridge (2006)
Bernardo, J., Berger, J., Dawid, A., Smith, A., et al.: Regression and classification using gaussian process priors (1998)
Vijaya Raghavan, S., Radhakrishnan, T., Srinivasan, K.: Soft sensor based composition estimation and controller design for an ideal reactive distillation column. ISA Transactions 50(1), 61–70 (2011)
Bahar, A., Giiner, E., Ozgen, C., Halici, U.: Design of state estimators for the inferential control of an industrial distillation column. In: International Joint Conference on Neural Networks, IJCNN 2006, pp. 1112–1115. IEEE (2006)
Mejdell, T., Skogestad, S.: Estimation of distillation compositions from multiple temperature measurements using partial-least-squares regression. Industrial & Engineering Chemistry Research 30(12), 2543–2555 (1991)
Kano, M., Miyazaki, K., Hasebe, S., Hashimoto, I.: Inferential control system of distillation compositions using dynamic partial least squares regression. Journal of Process Control 10(2), 157–166 (2000)
Kocijan, J.: Control algorithms based on gaussian process models: A state-of-the-art survey. In: Special International Conference on Complex Systems: Synergy of Control, Communications and Computing, vol. 16, pp. 273–280 (2011)
Åström, K.J.: Control system design lecture notes for me 155a. Department of Mechanical and Environmental Engineering University of California Santa Barbara (2002)
Reshef, D., Reshef, Y., Finucane, H., Grossman, S., McVean, G., Turnbaugh, P., Lander, E., Mitzenmacher, M., Sabeti, P.: Detecting novel associations in large data sets. Science 334(6062), 1518–1524 (2011)
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Abusnina, A., Kudenko, D., Roth, R. (2014). Gaussian Process-Based Inferential Control System. In: de la Puerta, J., et al. International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. Advances in Intelligent Systems and Computing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-07995-0_12
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DOI: https://doi.org/10.1007/978-3-319-07995-0_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07994-3
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