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dc.contributor.authorCardenas-Cabrera, Jorgespa
dc.contributor.authorDiaz-Charris, Luisspa
dc.contributor.authorTorres-Carvajal, Andrésspa
dc.contributor.authorCastro-Charris, Narcisospa
dc.contributor.authorRomero-Fandiño, Elenaspa
dc.contributor.authorRuiz Ariza, José Davidspa
dc.contributor.authorJiménez-Cabas, Javierspa
dc.date.accessioned2019-07-11T00:05:23Z
dc.date.available2019-07-11T00:05:23Z
dc.date.issued2019-04-09
dc.identifier.issn1687-5249spa
dc.identifier.issn1687-5257spa
dc.identifier.urihttp://hdl.handle.net/11323/4936spa
dc.description.abstractIn several industries using pipelines to transport different products from one point to another is a common and indispensable process, especially at oil/hydrocarbon industries. Thus, optimizing the way this process is carried out must be an issue that cannot be stopped. Therefore, the performance of the control strategy implemented is one way of reaching such optimal operating zones. This study proposes using Model Predictive Control strategies for solving some issues related to the proper operation of pipelines. It is proposed a model based on physics and thermodynamic laws, using MATLAB® as the development environment. This model involves four pumping stations separated by three pipeline sections. Three MPC strategies are developed and implemented. Accordingly, the results indicate that a centralized controller with an antiwindup back-calculation method has the best results among the three configurations used.spa
dc.language.isoeng
dc.publisherJournal of Control Science and Engineeringspa
dc.relation.ispartofhttps://doi.org/10.1155/2019/4538632spa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.titleModel Predictive Control Strategies Performance Evaluation over a Pipeline Transportation Systemspa
dc.typeArtículo de revistaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
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Barashkin, “Predictive control and suppression of pressure surges in main oil pipelines with counter-running pressure waves,” International Journal of Pressure Vessels and Piping, 2019. [6] A. J. Osiadacz and M. Chaczykowski, “Dynamic control for gas pipeline systems,” Archives of Mining Sciences, vol. 61, no. 1, pp. 69–82, 2016. [7] E. B. Priyanka, C. Maheswari, and S. Tangavel, “Online monitoring and control of fow rate in oil pipelines transportation system by using PLC-based Fuzzy-PID Controller,” Flow Measurement and Instrumentation, vol. 62, pp. 144–151, 2018. [8] M. Bauer and I. K. Craig, “Economic assessment of advanced process control—a survey and framework,” Journal of Process Control, vol. 18, no. 1, pp. 2–18, 2008. [9] X. Wang, B. Ding, X. Yang, and Z. Ye, “Design and application of ofset-free model predictive control disturbance observation method,” Journal of Control Science and Engineering, vol. 2016, Article ID 7279430, 8 pages, 2016. [10] D. G. Vale da Fonseca, A. F. 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Gregoritza, “Pipeline simulation techniques,” Mathematics and Computers in Simulation, vol. 52, no. 3-4, pp. 211–230, 2000. [20] S. Blazi ˇ c, D. Matko, and G. Geiger, “Simple model of a multi- ˇ batch driven pipeline,” Mathematics and Computers in Simulation, vol. 64, no. 6, pp. 617–630, 2004. [21] J. F. Noguera and S. Leirens, “Modelling and simulation of a multi-commodity pipeline network,” in Proceedings of the 2010 IEEE ANDESCON Conference, ANDESCON ’10, pp. 1–6, 2010. [22] J. J. Cabas and J. D. R. Ariza, “Modeling and simulation of a pipeline transportation process,” Journal of Engineering and Applied Sciences, vol. 13, no. 9, 2018. [23] L. Torres and C. Verde, “Modeling improvements for leak detection in pipelines of LPG,” in Proceedings of the 2013 European Control Conference, ECC ’13, pp. 938–942, 2013. [24] L. Wang, “Discrete model predictive controller design using Laguerre functions,” Journal of Process Control, vol. 14, no. 2, pp. 131–142, 2004. [25] M. S. Grewal and A. P. Andrews, Kalman Filtering: Teory and Practice Using MATLAB, John Wiley & Sons, 2011. [26] S. J. Qin and T. A. Badgwell, “A survey of industrial model predictive control technology,”Control Engineering Practice, vol. 11, no. 7, pp. 733–764, 2003. [27] J. Jimenez, L. Torres, C. Verde, and M. Sanju ´ an, “Friction esti- ´ mation of pipelines with extractions by using state observers,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 5361–5366, 2017spa
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa


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