A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms
) using the PSO algorithm. A thermodynamic model of the integrated system was developed from the application of mass, energy and exergy balances to each component, which allowed the calculation of the exergy destroyed a fraction of each equipment, the power generated, the thermal and exergetic effic...
- Autores:
-
Valencia, Guillermo
Duarte Forero, Jorge
Rojas Suárez, Jhan Piero
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Universidad Francisco de Paula Santander
- Repositorio:
- Repositorio Digital UFPS
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.ufps.edu.co:ufps/4883
- Acceso en línea:
- http://repositorio.ufps.edu.co/handle/ufps/4883
https://doi.org/10.1016/j.heliyon.2020.e04136
- Palabra clave:
- Energy
Mechanical engineering
Thermodynamics
Mathematical optimization
Energy conservation
Swarm intelligence algorithms
Brayton supercritical CO2 cycle
Organic Rankine cycle
Exergetic analysis
Energy analysis
- Rights
- openAccess
- License
- Under a Creative Commons license
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| dc.title.eng.fl_str_mv |
A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms |
| title |
A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms |
| spellingShingle |
A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms Energy Mechanical engineering Thermodynamics Mathematical optimization Energy conservation Swarm intelligence algorithms Brayton supercritical CO2 cycle Organic Rankine cycle Exergetic analysis Energy analysis |
| title_short |
A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms |
| title_full |
A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms |
| title_fullStr |
A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms |
| title_full_unstemmed |
A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms |
| title_sort |
A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms |
| dc.creator.fl_str_mv |
Valencia, Guillermo Duarte Forero, Jorge Rojas Suárez, Jhan Piero |
| dc.contributor.author.none.fl_str_mv |
Valencia, Guillermo Duarte Forero, Jorge Rojas Suárez, Jhan Piero |
| dc.subject.proposal.eng.fl_str_mv |
Energy Mechanical engineering Thermodynamics Mathematical optimization Energy conservation Swarm intelligence algorithms Brayton supercritical CO2 cycle Organic Rankine cycle Exergetic analysis Energy analysis |
| topic |
Energy Mechanical engineering Thermodynamics Mathematical optimization Energy conservation Swarm intelligence algorithms Brayton supercritical CO2 cycle Organic Rankine cycle Exergetic analysis Energy analysis |
| description |
) using the PSO algorithm. A thermodynamic model of the integrated system was developed from the application of mass, energy and exergy balances to each component, which allowed the calculation of the exergy destroyed a fraction of each equipment, the power generated, the thermal and exergetic efficiency of the system. In addition, through a sensitivity analysis, the effect of the main operational and design variables on thermal efficiency and total exergy destroyed was studied, which were the objective functions selected in the proposed optimization. The results show that the greatest exergy destruction occurs at the thermal source, with a value of 97 kW for the system without Reheater (NRH), but this is reduced by 92.28% for the system with Reheater (RH). In addition, by optimizing the integrated cycle for a particle number of 25, the maximum thermal efficiency of 55.53% (NRH) was achieved, and 56.95% in the RH system. Likewise, for a particle number of 15 and 20 in the PSO algorithm, exergy destruction was minimized to 60.72 kW (NRH) and 112.06 kW (RH), respectively. Comparative analyses of some swarm intelligence optimization algorithms were conducted for the integrated S-CO2-SORC system, evaluating performance indicators, where the PSO optimization algorithm was favorable in the analyses, guaranteeing that it is the ideal algorithm to solve this case study. |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020-06 |
| dc.date.accessioned.none.fl_str_mv |
2021-12-10T19:14:59Z |
| dc.date.available.none.fl_str_mv |
2021-12-10T19:14:59Z |
| dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_6501 |
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Text |
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info:eu-repo/semantics/article |
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http://purl.org/redcol/resource_type/ART |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/resource_type/c_6501 |
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publishedVersion |
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http://repositorio.ufps.edu.co/handle/ufps/4883 |
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https://doi.org/10.1016/j.heliyon.2020.e04136 |
| url |
http://repositorio.ufps.edu.co/handle/ufps/4883 https://doi.org/10.1016/j.heliyon.2020.e04136 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartof.none.fl_str_mv |
Heliyon |
| dc.relation.citationedition.spa.fl_str_mv |
Vol.6 No.6.(2020) |
| dc.relation.citationendpage.spa.fl_str_mv |
39 |
| dc.relation.citationissue.spa.fl_str_mv |
6 (2020) |
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1 |
| dc.relation.citationvolume.spa.fl_str_mv |
6 |
| dc.relation.cites.none.fl_str_mv |
Ochoa, G. V., Forero, J. D., & Rojas, J. P. (2020). A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms. Heliyon, 6(6), e04136. |
| dc.relation.ispartofjournal.spa.fl_str_mv |
Heliyon |
| dc.rights.eng.fl_str_mv |
Under a Creative Commons license |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.creativecommons.spa.fl_str_mv |
Atribución 4.0 Internacional (CC BY 4.0) |
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Under a Creative Commons license Atribución 4.0 Internacional (CC BY 4.0) http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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application/pdf |
| dc.publisher.spa.fl_str_mv |
Heliyon |
| dc.publisher.place.spa.fl_str_mv |
Amsterdam , Paises Bajos |
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https://www.cell.com/heliyon/fulltext/S2405-8440(20)30980-4?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2405844020309804%3Fshowall%3Dtrue#relatedArticles |
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Universidad Francisco de Paula Santander |
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Valencia, Guillermoa70459584c35a8074119d0e480ac044b600Duarte Forero, Jorge48f5c819166f76baf66b83fb1397bd6b600Rojas Suárez, Jhan Piero96cb752d974d2a7f4f66513af6ebbf8d6002021-12-10T19:14:59Z2021-12-10T19:14:59Z2020-06http://repositorio.ufps.edu.co/handle/ufps/4883https://doi.org/10.1016/j.heliyon.2020.e04136) using the PSO algorithm. A thermodynamic model of the integrated system was developed from the application of mass, energy and exergy balances to each component, which allowed the calculation of the exergy destroyed a fraction of each equipment, the power generated, the thermal and exergetic efficiency of the system. In addition, through a sensitivity analysis, the effect of the main operational and design variables on thermal efficiency and total exergy destroyed was studied, which were the objective functions selected in the proposed optimization. The results show that the greatest exergy destruction occurs at the thermal source, with a value of 97 kW for the system without Reheater (NRH), but this is reduced by 92.28% for the system with Reheater (RH). In addition, by optimizing the integrated cycle for a particle number of 25, the maximum thermal efficiency of 55.53% (NRH) was achieved, and 56.95% in the RH system. Likewise, for a particle number of 15 and 20 in the PSO algorithm, exergy destruction was minimized to 60.72 kW (NRH) and 112.06 kW (RH), respectively. Comparative analyses of some swarm intelligence optimization algorithms were conducted for the integrated S-CO2-SORC system, evaluating performance indicators, where the PSO optimization algorithm was favorable in the analyses, guaranteeing that it is the ideal algorithm to solve this case study.application/pdfengHeliyonAmsterdam , Paises BajosHeliyonVol.6 No.6.(2020)396 (2020)16Ochoa, G. V., Forero, J. D., & Rojas, J. P. (2020). A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms. Heliyon, 6(6), e04136.HeliyonUnder a Creative Commons licenseinfo:eu-repo/semantics/openAccessAtribución 4.0 Internacional (CC BY 4.0)http://purl.org/coar/access_right/c_abf2https://www.cell.com/heliyon/fulltext/S2405-8440(20)30980-4?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2405844020309804%3Fshowall%3Dtrue#relatedArticlesA comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithmsArtículo 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/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85EnergyMechanical engineeringThermodynamicsMathematical optimizationEnergy conservationSwarm intelligence algorithmsBrayton supercritical CO2 cycleOrganic Rankine cycleExergetic analysisEnergy analysisG. Valencia Ochoa, J. Nunez Alvarez, C. Acevedo Research Evolution on Renewable Energies Resources from 2007 to 2017: a Comparative Study on Solar, Geothermal, Wind and Biomass Energy (2019)G.V. Ochoa, J.N. Alvarez, M.V. Chamorro Data set on wind speed, wind direction and wind probability distributions in Puerto Bolivar-Colombia Data Br., 27 (2019), p. 104753F.B. Budes, G.V. Ochoa, Y.C. Escorcia An Economic Evaluation of Renewable and Conventional Electricity Generation Systems in a Shopping Center Using HOMER Pro® (2017)D. Milani, M.T. Luu, R. McNaughton, A. Abbas A comparative study of solar heliostat assisted supercritical CO2 recompression Brayton cycles: dynamic modelling and control strategies J. Supercrit. Fluids, 120 (2017), pp. 113-124G. Valencia, A. Fontalvo, Y. Cardenas Escorcia, J. Duarte, C. Isaza-Roldan Energy and exergy analysis of different exhaust waste heat recovery systems for natural gas engine based on ORC Energies, 12 (2019), p. 2378G. Valencia Ochoa, C. Acevedo Peñaloza, J. Duarte Forero Thermoeconomic optimization with PSO algorithm of waste heat recovery systems based on organic rankine cycle system for a natural gas engine Energies, 12 (21) (2019)G. Valencia, C. Peñaloza, J. Rojas Thermoeconomic modelling and parametric study of a Simple ORC for the recovery of waste heat in a 2 MW gas engine under different working fluids Appl. Sci., 9 (Oct. 2019), p. 4526S. Quoilin, M. Van Den Broek, S. Declaye, P. Dewallef, V. Lemort Techno-economic survey of organic rankine cycle (ORC) systems Renew. Sustain. Energy Rev., 22 (2013), pp. 168-186J. Dyreby, S. Klein, G. Nellis, D. Reindl Design considerations for supercritical carbon dioxide Brayton cycles with recompression J. Eng. Gas Turbines Power, 136 (10) (2014)P. Garg, P. Kumar, K. Srinivasan Supercritical carbon dioxide Brayton cycle for concentrated solar power J. Supercrit. Fluids, 76 (2013), pp. 54-60J. Sarkar Second law analysis of supercritical CO2 recompression Brayton cycle Energy, 34 (9) (2009), pp. 1172-1178M.S. Khan, M. Abid, H.M. Ali, K.P. Amber, M.A. Bashir, S. Javed Comparative performance assessment of solar dish assisted s-CO2 Brayton cycle using nanofluids Appl. Therm. Eng., 148 (2019), pp. 295-306L. Coco-Enríquez, J. Muñoz-Antón, J.M. Martínez-Val Integration between direct steam generation in linear solar collectors and supercritical carbon dioxide Brayton power cycles Int. J. Hydrogen Energy, 40 (44) (2015), pp. 15284-15300J. Wang, Z. Sun, Y. Dai, S. Ma Parametric optimization design for supercritical CO2 power cycle using genetic algorithm and artificial neural network Appl. Energy, 87 (4) (2010), pp. 1317-1324R. Chacartegui, J.M.M. De Escalona, D. Sánchez, B. Monje, T. Sánchez Alternative cycles based on carbon dioxide for central receiver solar power plants Appl. Therm. Eng., 31 (5) (2011), pp. 872-879S.J. Bae, Y. Ahn, J. Lee, J.I. Lee Hybrid system of Supercritical Carbon Dioxide Brayton cycle and carbon dioxide rankine cycle combined fuel cell In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition (2014)F. Alshammari, A. Pesyridis, A. Karvountzis-Kontakiotis, B. Franchetti, Y. Pesmazoglou Experimental study of a small scale organic Rankine cycle waste heat recovery system for a heavy duty diesel engine with focus on the radial inflow turbine expander performance Appl. Energy, 215 (2018), pp. 543-555G. Valencia, J. Núñez, J. Duarte Multiobjective optimization of a plate heat exchanger in a waste heat recovery organic rankine cycle system for natural gas engines Entropy, 21 (7) (2019)P. Song, M. Wei, L. Shi, S.N. Danish, C. Ma A review of scroll expanders for organic rankine cycle systems Appl. Therm. Eng., 75 (2015), pp. 54-64S. Quoilin, S. Declaye, B.F. Tchanche, V. Lemort Thermo-economic optimization of waste heat recovery Organic Rankine Cycles Appl. Therm. 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(2010), pp. 815-819ORIGINALA comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms.pdfA comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms.pdfapplication/pdf2761519https://repositorio.ufps.edu.co/bitstream/ufps/4883/1/A%20comparative%20energy%20and%20exergy%20optimization%20of%20a%20supercritical-CO2%20Brayton%20cycle%20and%20Organic%20Rankine%20Cycle%20combined%20system%20using%20swarm%20intelligence%20algorithms.pdf1cbbc5da2987d3f541beba006a376660MD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.ufps.edu.co/bitstream/ufps/4883/2/license.txt2f9959eaf5b71fae44bbf9ec84150c7aMD52open accessTEXTA comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms.pdf.txtA comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms.pdf.txtExtracted texttext/plain76786https://repositorio.ufps.edu.co/bitstream/ufps/4883/3/A%20comparative%20energy%20and%20exergy%20optimization%20of%20a%20supercritical-CO2%20Brayton%20cycle%20and%20Organic%20Rankine%20Cycle%20combined%20system%20using%20swarm%20intelligence%20algorithms.pdf.txt0cc93990c933e5690199b618c5cca1cdMD53open accessTHUMBNAILA comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms.pdf.jpgA comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms.pdf.jpgGenerated 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 incorporada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
0000-0001-5437-1964a70459584c35a8074119d0e480ac044b6000000-0001-7345-959048f5c819166f76baf66b83fb1397bd6b6000000-0003-2682-988096cb752d974d2a7f4f66513af6ebbf8d600 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