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Model Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System
dc.contributor.author | Cardenas-Cabrera, Jorge | spa |
dc.contributor.author | Diaz-Charris, Luis | spa |
dc.contributor.author | Torres-Carvajal, Andrés | spa |
dc.contributor.author | Castro-Charris, Narciso | spa |
dc.contributor.author | Romero-Fandiño, Elena | spa |
dc.contributor.author | Ruiz Ariza, José David | spa |
dc.contributor.author | Jiménez-Cabas, Javier | spa |
dc.date.accessioned | 2019-07-11T00:05:23Z | |
dc.date.available | 2019-07-11T00:05:23Z | |
dc.date.issued | 2019-04-09 | |
dc.identifier.issn | 1687-5249 | spa |
dc.identifier.issn | 1687-5257 | spa |
dc.identifier.uri | http://hdl.handle.net/11323/4936 | spa |
dc.description.abstract | In 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.iso | eng | |
dc.publisher | Journal of Control Science and Engineering | spa |
dc.relation.ispartof | https://doi.org/10.1155/2019/4538632 | spa |
dc.rights | CC0 1.0 Universal | spa |
dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | spa |
dc.title | Model Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System | spa |
dc.type | Artículo de revista | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.identifier.instname | Corporación Universidad de la Costa | spa |
dc.identifier.reponame | REDICUC - Repositorio CUC | spa |
dc.identifier.repourl | https://repositorio.cuc.edu.co/ | spa |
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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, 2017 | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | spa |
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