Parallel Computing for Rolling Mill Process with a Numerical Treatment of the LQR Problem
The considerable increase in computation of the optimal control problems has in many cases overflowed the computing capacity available to handle complex systems in real time. For this reason, alternatives such as parallel computing are studied in this article, where the problem is worked out by dist...
- Autores:
-
Gómez Múnera, John Anderson
Giraldo Quintero, Alejandro
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8869
- Acceso en línea:
- https://hdl.handle.net/11323/8869
https://doi.org/10.17981/cesta.01.01.2020.02
https://repositorio.cuc.edu.co/
- Palabra clave:
- Automatic control
Chemical processes
Computer programming
Computer techniques
Multithreading
Parallel algorithms
Parallel processing
Control automático
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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dc.title.spa.fl_str_mv |
Parallel Computing for Rolling Mill Process with a Numerical Treatment of the LQR Problem |
dc.title.translated.spa.fl_str_mv |
Computación en paralelo para el proceso de laminación con un tratamiento numérico del problema de LQR |
title |
Parallel Computing for Rolling Mill Process with a Numerical Treatment of the LQR Problem |
spellingShingle |
Parallel Computing for Rolling Mill Process with a Numerical Treatment of the LQR Problem Automatic control Chemical processes Computer programming Computer techniques Multithreading Parallel algorithms Parallel processing Control automático |
title_short |
Parallel Computing for Rolling Mill Process with a Numerical Treatment of the LQR Problem |
title_full |
Parallel Computing for Rolling Mill Process with a Numerical Treatment of the LQR Problem |
title_fullStr |
Parallel Computing for Rolling Mill Process with a Numerical Treatment of the LQR Problem |
title_full_unstemmed |
Parallel Computing for Rolling Mill Process with a Numerical Treatment of the LQR Problem |
title_sort |
Parallel Computing for Rolling Mill Process with a Numerical Treatment of the LQR Problem |
dc.creator.fl_str_mv |
Gómez Múnera, John Anderson Giraldo Quintero, Alejandro |
dc.contributor.author.spa.fl_str_mv |
Gómez Múnera, John Anderson Giraldo Quintero, Alejandro |
dc.subject.proposal.eng.fl_str_mv |
Automatic control Chemical processes Computer programming Computer techniques Multithreading Parallel algorithms Parallel processing |
topic |
Automatic control Chemical processes Computer programming Computer techniques Multithreading Parallel algorithms Parallel processing Control automático |
dc.subject.proposal.spa.fl_str_mv |
Control automático |
description |
The considerable increase in computation of the optimal control problems has in many cases overflowed the computing capacity available to handle complex systems in real time. For this reason, alternatives such as parallel computing are studied in this article, where the problem is worked out by distributing the tasks among several processors in order to accelerate the computation and to analyze and investigate the reduction of the total time of calculation the incremental gradually the processors used in it. We explore the use of these methods with a case study represented in a rolling mill process, and in turn making use of the strategy of updating the Phase Finals values for the construction of the final penalty matrix for the solution of the differential Riccati Equation. In addition, the order of the problem studied is increasing gradually for compare the improvements achieved in the models with major dimension. Parallel computing alternatives are also studied through multiple processing elements within a single machine or in a cluster via OpenMP, which is an application programming interface (API) that allows the creation of shared memory programs. |
publishDate |
2021 |
dc.date.accessioned.none.fl_str_mv |
2021-11-17T02:40:50Z |
dc.date.available.none.fl_str_mv |
2021-11-17T02:40:50Z |
dc.date.issued.none.fl_str_mv |
2021 |
dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
format |
http://purl.org/coar/resource_type/c_6501 |
status_str |
acceptedVersion |
dc.identifier.citation.spa.fl_str_mv |
J. A. Gómez & A. Giraldo-Quintero, “Parallel Computing for Rolling Mill Process with a Numerical Treatment of the LQR Problem”, J. Comput. Electron. Sci.: Theory Appl., vol. 1, no. 1, pp. 11–30, 2020. https://doi.org/10.17981/cesta.01.01.2020.02 |
dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/8869 |
dc.identifier.url.spa.fl_str_mv |
https://doi.org/10.17981/cesta.01.01.2020.02 |
dc.identifier.doi.spa.fl_str_mv |
10.17981/cesta.01.01.2020.02 |
dc.identifier.eissn.spa.fl_str_mv |
2745-0090 |
dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
identifier_str_mv |
J. A. Gómez & A. Giraldo-Quintero, “Parallel Computing for Rolling Mill Process with a Numerical Treatment of the LQR Problem”, J. Comput. Electron. Sci.: Theory Appl., vol. 1, no. 1, pp. 11–30, 2020. https://doi.org/10.17981/cesta.01.01.2020.02 10.17981/cesta.01.01.2020.02 2745-0090 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
url |
https://hdl.handle.net/11323/8869 https://doi.org/10.17981/cesta.01.01.2020.02 https://repositorio.cuc.edu.co/ |
dc.language.iso.none.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartofjournal.spa.fl_str_mv |
Computer and Electronic Sciences: Theory and Applications Computer and Electronic Sciences: Theory and Applications |
dc.relation.references.spa.fl_str_mv |
[1] V. E. Sonzogni, A. M. Yommi, N. M. Nigro & M. A. Storti, “A parallel finite element program on a beowulf cluster,” Adv Eng Softw, vol. 33, no. 7, pp. 427–443, Jul. 2002. https://doi.org/10.1016/S0965-9978(02)00059-5 [2] J. D. Hennessy & D. A. Patterson, Arquitectura de computadores: Un enfoque cuantitativo. MD, Esp.: McGraw-Hill, 1993. [3] M. Tokhi, M. A. Hossain & M. Shaheed, Parallel Computing for Real-Time Signal Processing and Control. London, UK: Springer, 2003. [4] A. S. Tanenbaum, Sistemas operativos modernos. 3rd edición. MX, D.F., MX: Pearson Educación, 2009. [5] P. Pardalos & R. Pytlak, Conjugate Gradient Algorithms In Nonconvex Optimization. NY, USA: Springer, 2008. [6] A. A. Agrachev & Y. L. Sachkov, Control Theory from the Geometric Viewpoint. Bln-HDB: Springer-Verlag, 2004. [7] M. Athans & P. Falb, Optimal Control: An Introduction to the Theory and Its Applications. NY, USA: Dover, 2006. [8] V. Costanza & P. S. Rivadeneira, Enfoque Hamiltoniano al control optimo de sistemas dinámicos. SAANZ, DE: OmniScriptum, 2014. [9] L. S. Pontryagin, V. G. Boltyanskii, R. V. Gamkrelidze & E. F. Mishchenko, The Mathematical Theory of Optimal Processes. NY, USA: Macmillan, 1964. [10] J. L. Troutman, Variational Calculus and Optimal Control. NY, USA: Springer, 1996. [11] V. Costanza & C. E. Neuman, “Partial differential equations for missing boundary conditions in the linear-quadratic optimal control problems,” Lat Am Appl Res, vol. 39, no. 3, pp. 207–212, Dec. 2009. Available: http://hdl.handle.net/11336/17096 [12] E. D. Sontag, Mathematical Control Theory. NY, USA: Springer, 1998. [13] V. Costanza & C. E. Neuman, “Optimal control of nonlinear chemical reactors via an initial-value hamiltonian problem,” Optim Control Appl Methods, vol. 27, no. 1, pp. 41–60, Jan. 2006. http://dx.doi.org/10.1002/oca.772 [14] A. Kojima & M. Morari, “LQ control for constrained continuous-time systems,” Automatica, vol. 40, no. 7, pp. 1143–1155, Jul. 2004. https://doi.org/10.1016/j.automatica.2004.02.007 [15] S. J. Qin & T. A. Badgwell, “A survey of industrial model predictive control technology,” Control Eng Pract, vol. 11, no. 7, pp. 733–764, Jul. 2003. https://doi.org/10.1016/S0967-0661(02)00186-7 [16] O. J. Rojas, G. C. Goodwin, M. M. Seron & A. Feuer, “An svd based´ strategy for receding horizon control of input constrained linear systems,” Int J Robust Nonlin, vol. 14, no. 13-14, pp. 1207–1226, May. 2004. https://doi.org/10.1002/rnc.940 [17] J. L. Speyer & D. H. Jacobson, Primer on Optimal Control Theory. Phila, USA: SIAM Books, 2010. [18] V. Costanza & P. S. Rivadeneira, “Optimal satured feedback laws for LQR problems with bounded controls,” Comput Appl Math, vol. 32, no. 2, pp. 355–371, Mar. 2013. https://doi.org/10.1007/s40314-013-0025-7 [19] V. Costanza, P. S. Rivadeneira & J. A. Gómez, “An efficient cost reduction procedure for bounded-control LQR problems,” Comput Appl Math, vol. 37, no. 2, pp. 1175–1196, Oct. 2016. https://doi.org/10.1007/s40314-016-0393-x [20] V. Costanza, P. S. Rivadeneira & J. A. Gómez, “Numerical treatment of the bounded-control lqr problem by updating the final phase value,” IEEE Lat Ame T, vol. 14, no. 6, pp. 2687–2692, Jun. 2016. https://doi.org/10.1109/TLA.2016.7555239 [21] V. Costanza & P. S. Rivadeneira, “Online suboptimal control of linearized models,” Syst Sci Control Eng, vol. 2, no. 1, pp. 379–388, Dec. 2014. https://doi.org/10.1080/21642583.2014.913215 [22] E. Bramanti, M. Bramanti, P. Stiavetti & E. Benedetti, “A frequency deconvolution procedure using a conjugate gradient minimization method with suitable constraints,” J Chemom, vol. 8, no. 6, pp. 409–421, Dec. 1994. https://doi.org/10.1002/ cem.1180080606 [23] R. Fletcher & C. M. Reeves, “Function minimization for conjugate gradients,” Computer J, vol. 7, no. 2, pp. 149–154, Jan. 1964. https://doi.org/10.1093/comjnl/7.2.149 [24] A. V. Rao, D. A. Benson, G. T. Huntington, C. Francolin, C. L. Darby & M. A. Patterson, “User’s manual for GPOPS: A matlab package for dynamic optimization using the gauss pseudospectral method,” UF, GVL; USA, Tech. Rep., Aug. 2008. [25] P. Bernhard, “Introduccion a la teoría de control Optimo,” Inst. Mat. Beppo Levi, ROS, AR, Tech. Rep., Cuaderno No. 4, 1972. [26] V. Costanza, P. S. Rivadeneira & R. D. Spies, “Equations for the missing boundary values in the hamiltonian formulation of optimal control problems,” J Optim Theory and Appl, vol. 149, no. 1, pp. 26–46, Jan. 2011. https://doi.org/10.1007/s10957-010- 9773-3 [27] G. C. Goodwin, S. F. Graebe & M. E. Salgado, Control system design, vol. 240. NJ, USA: Prentice Hall, 2001. [28] W. S. Levine, The control handbook. BOCCA, USA: CRC Press, 1996. [29] J.-J. E. Slotine & W. Li, Applied nonlinear control, vol. 199, no. 1. NJ, ENGI: Prentice-Hall, 1991. [30] V. Costanza & P. S. Rivadeneira, “Partially-Regular Bounded-Control Problems for Nonlinear Systems,” in Conf. Proc. XXIV Congreso Argentino de Control Automático, AADECA, BA, AR, 27-29 Oct. 2014. [31] J. A. Gómez, P. S. Rivadeneira & V. Costanza, “A cost reduction procedure for control-restricted nonlinear systems,” IREACO, vol. 10, no. 6, pp. 1–24, Nov. 2017. https://doi.org/10.15866/ireaco.v10i6.13820 [32] B. Chapman, G. Jost & R. Van Der Pas, Using OpenMP. CAM, MA, USA: MIT Press, 2008. [33] V. Dhamo & F. Troltzsch, “Some aspects of reachability for parabolic¨ boundary control problems with control constraints,” Comput Optim Appl, vol. 50, no. 1, pp. 75–110, Oct. 2008. https://doi.org/10.1007/s10589-009-9310-1 [34] G. Hearns & M. J. Grimble, “Temperature control in transport delay systems,” in Proc. 2010 American Control Conference, IEEE, Baltimore, MD, USA, 30 Jun-2 Jul. 2010, pp. 6089–6094. https://doi.org/10.1109/ACC.2010.5531540 [35] C. Sanderson & R. Curtin, “Armadillo: a template-based c++ library for linear algebra,” J Open Source Softw, vol. 1, no. 2, pp. 1–2, Jun. 2016. https://doi.org/10.21105/joss.00026 |
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Gómez Múnera, John AndersonGiraldo Quintero, Alejandro2021-11-17T02:40:50Z2021-11-17T02:40:50Z2021J. A. Gómez & A. Giraldo-Quintero, “Parallel Computing for Rolling Mill Process with a Numerical Treatment of the LQR Problem”, J. Comput. Electron. Sci.: Theory Appl., vol. 1, no. 1, pp. 11–30, 2020. https://doi.org/10.17981/cesta.01.01.2020.02https://hdl.handle.net/11323/8869https://doi.org/10.17981/cesta.01.01.2020.0210.17981/cesta.01.01.2020.022745-0090Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The considerable increase in computation of the optimal control problems has in many cases overflowed the computing capacity available to handle complex systems in real time. For this reason, alternatives such as parallel computing are studied in this article, where the problem is worked out by distributing the tasks among several processors in order to accelerate the computation and to analyze and investigate the reduction of the total time of calculation the incremental gradually the processors used in it. We explore the use of these methods with a case study represented in a rolling mill process, and in turn making use of the strategy of updating the Phase Finals values for the construction of the final penalty matrix for the solution of the differential Riccati Equation. In addition, the order of the problem studied is increasing gradually for compare the improvements achieved in the models with major dimension. Parallel computing alternatives are also studied through multiple processing elements within a single machine or in a cluster via OpenMP, which is an application programming interface (API) that allows the creation of shared memory programs.Gómez Múnera, John AndersonGiraldo Quintero, Alejandro20 páginasapplication/pdfengCorporación Universidad de la CostaBarranquillaAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© The author; licensee Universidad de la Costa - CUC.http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Computer and Electronic Sciences: Theory and Applicationshttps://revistascientificas.cuc.edu.co/CESTA/article/view/3376Parallel Computing for Rolling Mill Process with a Numerical Treatment of the LQR ProblemComputación en paralelo para el proceso de laminación con un tratamiento numérico del problema de LQRArtículo 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/acceptedVersionComputer and Electronic Sciences: Theory and ApplicationsComputer and Electronic Sciences: Theory and Applications[1] V. E. Sonzogni, A. M. Yommi, N. M. Nigro & M. A. Storti, “A parallel finite element program on a beowulf cluster,” Adv Eng Softw, vol. 33, no. 7, pp. 427–443, Jul. 2002. https://doi.org/10.1016/S0965-9978(02)00059-5[2] J. D. Hennessy & D. A. Patterson, Arquitectura de computadores: Un enfoque cuantitativo. MD, Esp.: McGraw-Hill, 1993.[3] M. Tokhi, M. A. Hossain & M. Shaheed, Parallel Computing for Real-Time Signal Processing and Control. London, UK: Springer, 2003.[4] A. S. Tanenbaum, Sistemas operativos modernos. 3rd edición. MX, D.F., MX: Pearson Educación, 2009.[5] P. Pardalos & R. Pytlak, Conjugate Gradient Algorithms In Nonconvex Optimization. NY, USA: Springer, 2008.[6] A. A. Agrachev & Y. L. Sachkov, Control Theory from the Geometric Viewpoint. Bln-HDB: Springer-Verlag, 2004.[7] M. Athans & P. Falb, Optimal Control: An Introduction to the Theory and Its Applications. NY, USA: Dover, 2006.[8] V. Costanza & P. S. Rivadeneira, Enfoque Hamiltoniano al control optimo de sistemas dinámicos. SAANZ, DE: OmniScriptum, 2014.[9] L. S. Pontryagin, V. G. Boltyanskii, R. V. Gamkrelidze & E. F. Mishchenko, The Mathematical Theory of Optimal Processes. NY, USA: Macmillan, 1964.[10] J. L. Troutman, Variational Calculus and Optimal Control. NY, USA: Springer, 1996.[11] V. Costanza & C. E. Neuman, “Partial differential equations for missing boundary conditions in the linear-quadratic optimal control problems,” Lat Am Appl Res, vol. 39, no. 3, pp. 207–212, Dec. 2009. Available: http://hdl.handle.net/11336/17096[12] E. D. Sontag, Mathematical Control Theory. NY, USA: Springer, 1998.[13] V. Costanza & C. E. Neuman, “Optimal control of nonlinear chemical reactors via an initial-value hamiltonian problem,” Optim Control Appl Methods, vol. 27, no. 1, pp. 41–60, Jan. 2006. http://dx.doi.org/10.1002/oca.772[14] A. Kojima & M. Morari, “LQ control for constrained continuous-time systems,” Automatica, vol. 40, no. 7, pp. 1143–1155, Jul. 2004. https://doi.org/10.1016/j.automatica.2004.02.007[15] S. J. Qin & T. A. Badgwell, “A survey of industrial model predictive control technology,” Control Eng Pract, vol. 11, no. 7, pp. 733–764, Jul. 2003. https://doi.org/10.1016/S0967-0661(02)00186-7[16] O. J. Rojas, G. C. Goodwin, M. M. Seron & A. Feuer, “An svd based´ strategy for receding horizon control of input constrained linear systems,” Int J Robust Nonlin, vol. 14, no. 13-14, pp. 1207–1226, May. 2004. https://doi.org/10.1002/rnc.940[17] J. L. Speyer & D. H. Jacobson, Primer on Optimal Control Theory. Phila, USA: SIAM Books, 2010.[18] V. Costanza & P. S. Rivadeneira, “Optimal satured feedback laws for LQR problems with bounded controls,” Comput Appl Math, vol. 32, no. 2, pp. 355–371, Mar. 2013. https://doi.org/10.1007/s40314-013-0025-7[19] V. Costanza, P. S. Rivadeneira & J. A. Gómez, “An efficient cost reduction procedure for bounded-control LQR problems,” Comput Appl Math, vol. 37, no. 2, pp. 1175–1196, Oct. 2016. https://doi.org/10.1007/s40314-016-0393-x[20] V. Costanza, P. S. Rivadeneira & J. A. Gómez, “Numerical treatment of the bounded-control lqr problem by updating the final phase value,” IEEE Lat Ame T, vol. 14, no. 6, pp. 2687–2692, Jun. 2016. https://doi.org/10.1109/TLA.2016.7555239[21] V. Costanza & P. S. Rivadeneira, “Online suboptimal control of linearized models,” Syst Sci Control Eng, vol. 2, no. 1, pp. 379–388, Dec. 2014. https://doi.org/10.1080/21642583.2014.913215[22] E. Bramanti, M. Bramanti, P. Stiavetti & E. Benedetti, “A frequency deconvolution procedure using a conjugate gradient minimization method with suitable constraints,” J Chemom, vol. 8, no. 6, pp. 409–421, Dec. 1994. https://doi.org/10.1002/ cem.1180080606[23] R. Fletcher & C. M. Reeves, “Function minimization for conjugate gradients,” Computer J, vol. 7, no. 2, pp. 149–154, Jan. 1964. https://doi.org/10.1093/comjnl/7.2.149[24] A. V. Rao, D. A. Benson, G. T. Huntington, C. Francolin, C. L. Darby & M. A. Patterson, “User’s manual for GPOPS: A matlab package for dynamic optimization using the gauss pseudospectral method,” UF, GVL; USA, Tech. Rep., Aug. 2008.[25] P. Bernhard, “Introduccion a la teoría de control Optimo,” Inst. Mat. Beppo Levi, ROS, AR, Tech. Rep., Cuaderno No. 4, 1972.[26] V. Costanza, P. S. Rivadeneira & R. D. Spies, “Equations for the missing boundary values in the hamiltonian formulation of optimal control problems,” J Optim Theory and Appl, vol. 149, no. 1, pp. 26–46, Jan. 2011. https://doi.org/10.1007/s10957-010- 9773-3[27] G. C. Goodwin, S. F. Graebe & M. E. Salgado, Control system design, vol. 240. NJ, USA: Prentice Hall, 2001.[28] W. S. Levine, The control handbook. BOCCA, USA: CRC Press, 1996.[29] J.-J. E. Slotine & W. Li, Applied nonlinear control, vol. 199, no. 1. NJ, ENGI: Prentice-Hall, 1991.[30] V. Costanza & P. S. Rivadeneira, “Partially-Regular Bounded-Control Problems for Nonlinear Systems,” in Conf. Proc. XXIV Congreso Argentino de Control Automático, AADECA, BA, AR, 27-29 Oct. 2014.[31] J. A. Gómez, P. S. Rivadeneira & V. Costanza, “A cost reduction procedure for control-restricted nonlinear systems,” IREACO, vol. 10, no. 6, pp. 1–24, Nov. 2017. https://doi.org/10.15866/ireaco.v10i6.13820[32] B. Chapman, G. Jost & R. Van Der Pas, Using OpenMP. CAM, MA, USA: MIT Press, 2008.[33] V. Dhamo & F. Troltzsch, “Some aspects of reachability for parabolic¨ boundary control problems with control constraints,” Comput Optim Appl, vol. 50, no. 1, pp. 75–110, Oct. 2008. https://doi.org/10.1007/s10589-009-9310-1[34] G. Hearns & M. J. Grimble, “Temperature control in transport delay systems,” in Proc. 2010 American Control Conference, IEEE, Baltimore, MD, USA, 30 Jun-2 Jul. 2010, pp. 6089–6094. https://doi.org/10.1109/ACC.2010.5531540[35] C. Sanderson & R. Curtin, “Armadillo: a template-based c++ library for linear algebra,” J Open Source Softw, vol. 1, no. 2, pp. 1–2, Jun. 2016. https://doi.org/10.21105/joss.00026301111CESTAAutomatic controlChemical processesComputer programmingComputer techniquesMultithreadingParallel algorithmsParallel processingControl automáticoPublicationORIGINALVol1-02.pdfVol1-02.pdfapplication/pdf1506057https://repositorio.cuc.edu.co/bitstreams/ddceda7f-67b5-497e-8758-764715591108/downloadc6a9e2f326d62dcc813dba3896c2317eMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/0a60490b-2757-4c25-85ef-dc5a88e6df39/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; 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