Model Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System

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...

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Autores:
Cardenas-Cabrera, Jorge
Diaz-Charris, Luis
Torres-Carvajal, Andrés
Castro-Charris, Narciso
Romero-Fandiño, Elena
Ruiz Ariza, José David
Jiménez-Cabas, Javier
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/4936
Acceso en línea:
https://hdl.handle.net/11323/4936
https://repositorio.cuc.edu.co/
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openAccess
License
CC0 1.0 Universal
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repository_id_str
dc.title.spa.fl_str_mv Model Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System
title Model Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System
spellingShingle Model Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System
title_short Model Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System
title_full Model Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System
title_fullStr Model Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System
title_full_unstemmed Model Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System
title_sort Model Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System
dc.creator.fl_str_mv Cardenas-Cabrera, Jorge
Diaz-Charris, Luis
Torres-Carvajal, Andrés
Castro-Charris, Narciso
Romero-Fandiño, Elena
Ruiz Ariza, José David
Jiménez-Cabas, Javier
dc.contributor.author.spa.fl_str_mv Cardenas-Cabrera, Jorge
Diaz-Charris, Luis
Torres-Carvajal, Andrés
Castro-Charris, Narciso
Romero-Fandiño, Elena
Ruiz Ariza, José David
Jiménez-Cabas, Javier
description 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.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-07-11T00:05:23Z
dc.date.available.none.fl_str_mv 2019-07-11T00:05:23Z
dc.date.issued.none.fl_str_mv 2019-04-09
dc.type.spa.fl_str_mv Artículo de revista
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dc.language.iso.none.fl_str_mv eng
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dc.relation.ispartof.spa.fl_str_mv https://doi.org/10.1155/2019/4538632
dc.relation.references.spa.fl_str_mv [1] J. A. Jimenez Cabas, M. Sanju ´ an, and L. Torres, ´ Liquid Transport Pipeline Monitoring Architecture Based on State Estimators for Leak Detection and Location, Universidad del Norte, 2018. [2] R. Tubb, “P&GJ’s 2017 worldwide pipeline construction report,” Pipeline & Gas Journal, 2017. [3] V. Tang Pham, D. Georges, G. Besanc¸on et al., “Predictive control with guaranteed stability for hyperbolic systems of conservation laws,” in Proceedings of the49th IEEE Conference on Decision and Control (CDC), pp. 6932–6937, 2010. [4] T. V. Pham, D. Georges, and G. Besancon, “Predictive control with guaranteed stability for water hammer equations,” Institute of Electrical and Electronics Engineers Transactions on Automatic Control, vol. 59, no. 2, pp. 465–470, 2014. [5] V. Yuzhanin, V. Popadko, T. Koturbash, V. Chernova, and R. 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. Dantas, C. E. Dorea, and A. L. ´ Maitelli, “Explicit GPC control applied to an approximated linearized crane system,” Journal of Control Science and Engineering, vol. 2019, Article ID 3612634, 13 pages, 2019. [11] J. Duarte, J. Garcia, J. Jimenez, M. E. Sanjuan, A. Bula, and ´ J. Gonzalez, “Auto-ignition control in spark-ignition engines ´ using internal model control structure,” Journal of Energy Resource Technology, vol. 139, no. 2, p. 22201, 2017. [12] M. Ławrynczuk, ´ Computationally Efcient Model Predictive Control Algorithms, Springer, 2014. [13] G. Lars and P. Jurgen, ¨ Nonlinear Model Predictive ControlTeory and Algorithms, Springer, 2011. [14] Y. G. Xi, D. W. Li, and S. Lin, “Model predictive control — status and challenges,” Acta Automatica Sinica, vol. 39, no. 3, pp. 222– 236, 2013. [15] J. Jimenez, L. Torres, I. Rubio, and M. Sanjuan, “Auxiliary signal ´ design and lienard-type models for identifying pipeline,” in ´ Modeling and Monitoring of Pipelines and Networks: Advanced Tools for Automatic Monitoring and Supervision of Pipelines, C. Verde and L. Torres, Eds., pp. 99–124, Springer, 2017. [16] J. Jimenez-Cabas, L. Torres, F. R. Lopez-Estrada, and M. Sanjuan, “Leak diagnosis in pipelines by only using fow measurements,” in Proceedings of the IEEE Colombian Conference on Automatic Control (CCAC), 2017. [17] L. Torres, G. Besanc¸on, A. Navarro, O. Begovich, and D. Georges, “Examples of pipeline monitoring with nonlinear observers and real-data validation,” in Proceedings of the 8th IEEE Int. Multi-Conf Signals Syst. Devices, pp. 1–6, 2011. [18] J. Jimenez-Cabas, E. Romero-Fandi ´ no, L. Torres, M. Sanjuan, ˜ and F. R. Lopez-Estrada, “Localization of leaks in water distri- ´ bution networks using fow readings,” IFAC-PapersOnLine, vol. 51, no. 24, pp. 922–928, 2018. [19] D. Matko, G. Geiger, and W. 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, 2017
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spelling Cardenas-Cabrera, JorgeDiaz-Charris, LuisTorres-Carvajal, AndrésCastro-Charris, NarcisoRomero-Fandiño, ElenaRuiz Ariza, José DavidJiménez-Cabas, Javier2019-07-11T00:05:23Z2019-07-11T00:05:23Z2019-04-091687-52491687-5257https://hdl.handle.net/11323/4936Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.Cardenas-Cabrera, JorgeDiaz-Charris, LuisTorres-Carvajal, AndrésCastro-Charris, NarcisoRomero-Fandiño, ElenaRuiz Ariza, José DavidJiménez-Cabas, Javier-0000-0001-9707-8418-600engJournal of Control Science and Engineeringhttps://doi.org/10.1155/2019/4538632[1] J. A. Jimenez Cabas, M. Sanju ´ an, and L. Torres, ´ Liquid Transport Pipeline Monitoring Architecture Based on State Estimators for Leak Detection and Location, Universidad del Norte, 2018. [2] R. Tubb, “P&GJ’s 2017 worldwide pipeline construction report,” Pipeline & Gas Journal, 2017. [3] V. Tang Pham, D. Georges, G. Besanc¸on et al., “Predictive control with guaranteed stability for hyperbolic systems of conservation laws,” in Proceedings of the49th IEEE Conference on Decision and Control (CDC), pp. 6932–6937, 2010. [4] T. V. Pham, D. Georges, and G. Besancon, “Predictive control with guaranteed stability for water hammer equations,” Institute of Electrical and Electronics Engineers Transactions on Automatic Control, vol. 59, no. 2, pp. 465–470, 2014. [5] V. Yuzhanin, V. Popadko, T. Koturbash, V. Chernova, and R. 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. Dantas, C. E. Dorea, and A. L. ´ Maitelli, “Explicit GPC control applied to an approximated linearized crane system,” Journal of Control Science and Engineering, vol. 2019, Article ID 3612634, 13 pages, 2019. [11] J. Duarte, J. Garcia, J. Jimenez, M. E. Sanjuan, A. Bula, and ´ J. Gonzalez, “Auto-ignition control in spark-ignition engines ´ using internal model control structure,” Journal of Energy Resource Technology, vol. 139, no. 2, p. 22201, 2017. [12] M. Ławrynczuk, ´ Computationally Efcient Model Predictive Control Algorithms, Springer, 2014. [13] G. Lars and P. Jurgen, ¨ Nonlinear Model Predictive ControlTeory and Algorithms, Springer, 2011. [14] Y. G. Xi, D. W. Li, and S. Lin, “Model predictive control — status and challenges,” Acta Automatica Sinica, vol. 39, no. 3, pp. 222– 236, 2013. [15] J. Jimenez, L. Torres, I. Rubio, and M. Sanjuan, “Auxiliary signal ´ design and lienard-type models for identifying pipeline,” in ´ Modeling and Monitoring of Pipelines and Networks: Advanced Tools for Automatic Monitoring and Supervision of Pipelines, C. Verde and L. Torres, Eds., pp. 99–124, Springer, 2017. [16] J. Jimenez-Cabas, L. Torres, F. R. Lopez-Estrada, and M. Sanjuan, “Leak diagnosis in pipelines by only using fow measurements,” in Proceedings of the IEEE Colombian Conference on Automatic Control (CCAC), 2017. [17] L. Torres, G. Besanc¸on, A. Navarro, O. Begovich, and D. Georges, “Examples of pipeline monitoring with nonlinear observers and real-data validation,” in Proceedings of the 8th IEEE Int. Multi-Conf Signals Syst. Devices, pp. 1–6, 2011. [18] J. Jimenez-Cabas, E. Romero-Fandi ´ no, L. Torres, M. Sanjuan, ˜ and F. R. Lopez-Estrada, “Localization of leaks in water distri- ´ bution networks using fow readings,” IFAC-PapersOnLine, vol. 51, no. 24, pp. 922–928, 2018. [19] D. Matko, G. Geiger, and W. 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, 2017CC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Model Predictive Control Strategies Performance Evaluation over a Pipeline Transportation SystemArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionPublicationORIGINALModel Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System.pdfModel Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System.pdfapplication/pdf1897607https://repositorio.cuc.edu.co/bitstreams/ebf3dd0e-db35-4eb4-9834-6ee578ebe620/download8d0837dd4b18cc0e1fcad26b89756a99MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/32ec5fe2-87ba-4e11-b71b-674f488b5684/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/8449c9e1-2160-48e4-afe6-ceb5d6c14f6a/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILModel Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System.pdf.jpgModel Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System.pdf.jpgimage/jpeg59484https://repositorio.cuc.edu.co/bitstreams/da31b9da-9414-4ce6-bdde-e61d71e0ea0e/downloaded753cbfc493a2e03dc143a55556eafeMD55TEXTModel Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System.pdf.txtModel Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System.pdf.txttext/plain40443https://repositorio.cuc.edu.co/bitstreams/1b6149bb-636b-431c-b4e5-91ef467887d7/download1a61020a8ac54de7b51448b630a98fb7MD5611323/4936oai:repositorio.cuc.edu.co:11323/49362024-09-17 11:07:54.497http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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