Procedure to obtain the optimal distribution cooling capacity of an air-condensed chiller plant for a hotel facility conceptual design
The article presents a novel methodology for designing chiller plants for a hotel facility to determine the optimal distribution of the chillers cooling capacity that compose the plant. The methodology proposes three phases. In the first, the statistical analysis allowed to determine the cooling dem...
- Autores:
-
diaz torres, yamile
Reyes Calvo, Roy
Hernández Herrera, Hernán
Álvarez Guerra, Mario A.
Gómez Sarduy, Julio Rafael
Silva Ortega, Jorge I
- 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:11323/8973
- Acceso en línea:
- https://hdl.handle.net/11323/8973
https://doi.org/10.1016/j.egyr.2021.07.090
https://repositorio.cuc.edu.co/
- Palabra clave:
- Genetic algorithm
Chiller plant
Cooling capacity
Optimal chiller loading and sequence
Hotel facilities
- Rights
- openAccess
- License
- CC0 1.0 Universal
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| dc.title.spa.fl_str_mv |
Procedure to obtain the optimal distribution cooling capacity of an air-condensed chiller plant for a hotel facility conceptual design |
| title |
Procedure to obtain the optimal distribution cooling capacity of an air-condensed chiller plant for a hotel facility conceptual design |
| spellingShingle |
Procedure to obtain the optimal distribution cooling capacity of an air-condensed chiller plant for a hotel facility conceptual design Genetic algorithm Chiller plant Cooling capacity Optimal chiller loading and sequence Hotel facilities |
| title_short |
Procedure to obtain the optimal distribution cooling capacity of an air-condensed chiller plant for a hotel facility conceptual design |
| title_full |
Procedure to obtain the optimal distribution cooling capacity of an air-condensed chiller plant for a hotel facility conceptual design |
| title_fullStr |
Procedure to obtain the optimal distribution cooling capacity of an air-condensed chiller plant for a hotel facility conceptual design |
| title_full_unstemmed |
Procedure to obtain the optimal distribution cooling capacity of an air-condensed chiller plant for a hotel facility conceptual design |
| title_sort |
Procedure to obtain the optimal distribution cooling capacity of an air-condensed chiller plant for a hotel facility conceptual design |
| dc.creator.fl_str_mv |
diaz torres, yamile Reyes Calvo, Roy Hernández Herrera, Hernán Álvarez Guerra, Mario A. Gómez Sarduy, Julio Rafael Silva Ortega, Jorge I |
| dc.contributor.author.spa.fl_str_mv |
diaz torres, yamile Reyes Calvo, Roy Hernández Herrera, Hernán Álvarez Guerra, Mario A. Gómez Sarduy, Julio Rafael Silva Ortega, Jorge I |
| dc.subject.spa.fl_str_mv |
Genetic algorithm Chiller plant Cooling capacity Optimal chiller loading and sequence Hotel facilities |
| topic |
Genetic algorithm Chiller plant Cooling capacity Optimal chiller loading and sequence Hotel facilities |
| description |
The article presents a novel methodology for designing chiller plants for a hotel facility to determine the optimal distribution of the chillers cooling capacity that compose the plant. The methodology proposes three phases. In the first, the statistical analysis allowed to determine the cooling demand required in the facility, where the constructed thermal demand profiles reflect future operating conditions, and to obtain the individual cooling capacities of the chillers. In the second phase, the black box models were built to simulate the chillers energy performance and, using a mathematical algorithm allows to obtain a combination of chiller plants. The third phase constitutes the energy evaluation through the solution of a mathematical optimization problem and using a genetic algorithm. This was carried out under the sequence approach and the optimal load of each machine against the working conditions. This analysis allows calculating the performance, the life cycle cost, and the indirect environmental impact. The paper proposes a case study to demonstrate the feasibility of applying the methodology to the initial design stage, achieving a saving of 14,4%. Finally, using statistical analysis, the method allows comparing the relationship between each chiller plant considering the design and operating parameters. |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2022-01-16T20:22:29Z |
| dc.date.available.none.fl_str_mv |
2022-01-16T20:22:29Z |
| 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/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 |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_6501 |
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acceptedVersion |
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2352-4847 |
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https://hdl.handle.net/11323/8973 |
| dc.identifier.doi.spa.fl_str_mv |
https://doi.org/10.1016/j.egyr.2021.07.090 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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2352-4847 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/8973 https://doi.org/10.1016/j.egyr.2021.07.090 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
[1] L.M. VegaB Castellanos, Yanez JP. Modeling and identification of the cooling dynamics of a tropical island hotel. Energy Build 2015;92:9–28. [2] Ji-Hye R, Won-Hwa H, Youn-Kyu S. Characteristic analysis of peak load in electricity on large scale hotels considering the energy efficiency. Int J Smart Home 2014;8(3):207–22. [3] Wang F, Lin H, Tu W, Wang Y, Huang Y. Energy modeling and chillers sizing of HVAC system for a hotel building. Procedia Eng 2015;121:1812–8. [4] Gang W, Wang S, Augenbroe G, Xiao F. Robust optimal design of district cooling systems and the impacts of uncertainty and reliability. Energy Build 2016;122:11–22. [5] Yu FW, Chan KT. Strategy for designing more energy efficient chiller plants serving air-conditioned buildings. Build Environ 2007;42(10):3737–46. [6] Torres YD, Plasencia MÁG, Felipe PRV, Sánchez GC, González MD. Chiller plant design. Review of the aspects that involve its efficient design. Ing Energética 2020;41(1):7. [7] ANSI/ASHRAE/IES Standard 901-2013. Energy Standard for buildings Except Low-Rise Residential Buildings. I-P edition. [8] Chan RK, Lee EW, Yuen RK. An integrated model for the design of air-cooled chiller plants for commercial buildings. Build Environ 2011;46(1):196–209. [9] Taylor S. Fundamentals of design and control of central chilled-water plans (IP). Atlanta ASHRAE; 2017, Capitulo 6. [consulted 20 2020]. [10] Bitondo M, Tosí J. Chiller control plant. Syracuse. New Cork. EUA: Carrier Corporation; 1999. [11] Haviland JR. Central plant retrofit considerations. Energy Eng 2002;99(3):48–58. [12] Mathew P, Greenberg S. Labs21 sustainable design programming checklist version 1.0. (No. LBNL-55506). Berkeley, CA (US): Ernest Orlando Lawrence Berkeley National Laboratory; 2005. [13] Stanford III HW. HVAC water chillers and cooling towers: Fundamentals, application, and operation. CRC Press; 2011. [14] Yu FW, Chan KT. Low-energy design for air-cooled chiller plants in air-conditioned buildings. Energy Build 2006;38(4):334–9. [15] Gang W, Wang S, Xiao F, Gao DC. Robust optimal design of building cooling systems considering cooling load uncertainty and equipment reliability. Appl Energy 2015a;159:265–75. [16] Gang W, Wang S, Yan C, Xiao F. Robust optimal design of building cooling systems concerning uncertainties using mini-max regret theory. Sci Technol Built Environ 2015b;21(6):789–99. [17] Cheng Q, Yan C, Wang S. Robust optimal design of chiller plants based on cooling load distribution. Energy Procedia 2015;75:1354–9. [18] Kang Y, Augenbroe G, Li Q, Wang Q. Effects of scenario uncertainty on chiller sizing method. Appl Therm Eng 2017;123:187–95. [19] Cheng Q, Wang S, Yan C, Xiao F. Probabilistic approach for uncertainty-based optimal design of chiller plants in buildings. Appl Energy 2017;185:1613–24. [20] Huang P, Huang G, Augenbroe G, Li S. Optimal configuration of multiple-chiller plants under cooling load uncertainty for different climate effects and building types. Energy Build 2018;158:684–97. [21] Li H, Wang S, Xiao F. Probabilistic optimal design and on-site adaptive commissioning of building air-conditioning systems concerning uncertainties. Energy Procedia 2019;158:2725–30. [22] Shiming D. Sizing replacement chiller plants2. ASHRAE J 2002;44(6):47. [23] Zheng ZX, Li JQ. Optimal chiller loading by improved invasive weed optimization algorithm for reducing energy consumption. Energy Build 2018;161:80–8. [24] Zheng ZX, Li JQ, Duan PY. Optimal chiller loading by improved artificial fish swarm algorithm for energy saving. Math Comput Simulation 2019;155:227–43. [25] Handbook, ASHRAE. Fundamentals. In: Ventilating and air-conditioning engineers. ASHRAE–American Society of Heating; 2017. [26] Torres YD, Herrera HH, Plasencia MAAG, Novo EP, Cabrera LP, et al. Heating ventilation and air-conditioned configurations for hotels an approach review for the design and exploitation. Energy Rep 2020a;6:487–97. [27] Lapin L. Statistics: Meaning and method. 2nd ed.. Harcourt Brace Jovanovich; 1980. [28] Correia R, Diaz Y. Análisis estadístico de perfiles de demanda térmica (Tesis de Grado), Cuba: Universidad de Cienfuegos.; 2020. [29] Torres YD, Justiz MS, Francisco JL, Lazo DÁ, Torres YM, Guerra MA. Methodology for the preparation and selection of black box mathematical models for the energy simulation of screw type chillers. Ing Mec 2020b;23(3):1–6. [30] Sun Y, Wang S, Xiao F. In situ performance comparison and evaluation of three chiller sequencing control strategies in a super high-rise building. Energy Build 2013;61:333–43. [31] Huang S, Zuo W, Sohn MD. A new method for the optimal chiller sequencing control. In: Proceedings of the 14th conference of IBPSA. Hyderabad, India: 2015. p. 316–23. [32] Chang YC, Lin FA, Lin CH. Optimal chiller sequencing by branch and bound method for saving energy. Energy Convers Manage 2005;46(13–14):2158–72. [33] Tredinnick S. Inside insights-Benefits of economic analyses (part 2): Real-world examples. Dist Energy 2011;97(3):66. [34] CIBSE A. Environmental design. The Chartered Institution of Building Services Engineers London; 2018. [35] Montelier S. Reducción del consumo de energía en instalaciones con sistemas de climatización centralizada Todo Agua a Flujo constante (Tesis Doctoral), Cuba: Universidad de Cienfuegos; 2008. [36] Cuza V. Estudio energético del Sistema de climatización del Hotel Jagua (Tesis de maestría), Cuba: Universidad de Cienfuegos; 2010. [37] Díaz Y, Monteagudo JP, Bravo D. Análisis energético de un sistema híbrido de producción de frío. Ing Energética 2015;36(1):38–49. [38] Díaz Y, Valdivia Y, Monteagudo JP, Miranda Y. Application of building energy simulation in the validation of operational strategies of HVAC systems on a tropical hotel. Ing Mec 2017;20(1):31–8. [39] Valdivia Nodal Y, Díaz Torres Y, Lapido Rodríguez M. Alternativas de producción de agua caliente sanitaria en instalaciones hoteleras con climatización centralizada. Rev Univ Soc 2015;7(3):88–94. [40] Udawatta L, Perera A, Witharana S. Analysis of sensory information for efficient operation of energy management systems in commercial hotels2. Electron J Struct Eng 2010;11:3–120. [41] Yang Z, Ghahramani A, Becerik-Gerber B. Building occupancy diversity and HVAC (heating, ventilation, and air conditioning) system energy efficiency. Energy 2016;109:641–9. [42] Shenzhen Hero-Tech Refrigeration Equipment Co, Ltd. 2020, https://hero-tech.en.made-in-china.com/product-group/LMQJDqIlsgkP/Air -cooled-screw-chiller-catalog-2.html. [43] Cardoso AI, García DM, García JF. Evaluación de la factibilidad económico-financiera del proyecto de inversión. Centro Cultural Julio Antonio Mella. Cienfuegos. Univ Soc 2019;11(5):8–18. [44] Meneses E, Roig A, Paz E, Alonso D, Alvarado J. Factores de emisión de CO, CO2, NOx y SO2 para instalaciones generadoras de electricidad en Cuba. Rev Cuba Meteorol 2018;24(1):1–9. |
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diaz torres, yamile6eec707af4d94d1051c36b8f4796e8edReyes Calvo, Roy78045fb2f4c669c5c1dedd4aad597912Hernández Herrera, Hernán368f702324432a4013403d8c819e2e2eÁlvarez Guerra, Mario A.2d1793b95bc6eb502058da79a1e0b6cfGómez Sarduy, Julio Rafaelfa6b9fffd0fa386bfadfeb4343a2af8aSilva Ortega, Jorge I83b55aee69e99cf41061bfcc3130d4f32022-01-16T20:22:29Z2022-01-16T20:22:29Z20212352-4847https://hdl.handle.net/11323/8973https://doi.org/10.1016/j.egyr.2021.07.090Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The article presents a novel methodology for designing chiller plants for a hotel facility to determine the optimal distribution of the chillers cooling capacity that compose the plant. The methodology proposes three phases. In the first, the statistical analysis allowed to determine the cooling demand required in the facility, where the constructed thermal demand profiles reflect future operating conditions, and to obtain the individual cooling capacities of the chillers. In the second phase, the black box models were built to simulate the chillers energy performance and, using a mathematical algorithm allows to obtain a combination of chiller plants. The third phase constitutes the energy evaluation through the solution of a mathematical optimization problem and using a genetic algorithm. This was carried out under the sequence approach and the optimal load of each machine against the working conditions. This analysis allows calculating the performance, the life cycle cost, and the indirect environmental impact. The paper proposes a case study to demonstrate the feasibility of applying the methodology to the initial design stage, achieving a saving of 14,4%. Finally, using statistical analysis, the method allows comparing the relationship between each chiller plant considering the design and operating parameters.application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Energy Reportshttps://www.sciencedirect.com/science/article/pii/S2352484721005539Genetic algorithmChiller plantCooling capacityOptimal chiller loading and sequenceHotel facilitiesProcedure to obtain the optimal distribution cooling capacity of an air-condensed chiller plant for a hotel facility conceptual designArtículo 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/acceptedVersion[1] L.M. VegaB Castellanos, Yanez JP. Modeling and identification of the cooling dynamics of a tropical island hotel. Energy Build 2015;92:9–28.[2] Ji-Hye R, Won-Hwa H, Youn-Kyu S. Characteristic analysis of peak load in electricity on large scale hotels considering the energy efficiency. Int J Smart Home 2014;8(3):207–22.[3] Wang F, Lin H, Tu W, Wang Y, Huang Y. Energy modeling and chillers sizing of HVAC system for a hotel building. Procedia Eng 2015;121:1812–8.[4] Gang W, Wang S, Augenbroe G, Xiao F. Robust optimal design of district cooling systems and the impacts of uncertainty and reliability. Energy Build 2016;122:11–22.[5] Yu FW, Chan KT. Strategy for designing more energy efficient chiller plants serving air-conditioned buildings. Build Environ 2007;42(10):3737–46.[6] Torres YD, Plasencia MÁG, Felipe PRV, Sánchez GC, González MD. Chiller plant design. Review of the aspects that involve its efficient design. Ing Energética 2020;41(1):7.[7] ANSI/ASHRAE/IES Standard 901-2013. Energy Standard for buildings Except Low-Rise Residential Buildings. I-P edition.[8] Chan RK, Lee EW, Yuen RK. An integrated model for the design of air-cooled chiller plants for commercial buildings. Build Environ 2011;46(1):196–209.[9] Taylor S. Fundamentals of design and control of central chilled-water plans (IP). Atlanta ASHRAE; 2017, Capitulo 6. [consulted 20 2020].[10] Bitondo M, Tosí J. Chiller control plant. Syracuse. New Cork. EUA: Carrier Corporation; 1999.[11] Haviland JR. Central plant retrofit considerations. Energy Eng 2002;99(3):48–58.[12] Mathew P, Greenberg S. Labs21 sustainable design programming checklist version 1.0. (No. LBNL-55506). Berkeley, CA (US): Ernest Orlando Lawrence Berkeley National Laboratory; 2005.[13] Stanford III HW. HVAC water chillers and cooling towers: Fundamentals, application, and operation. CRC Press; 2011.[14] Yu FW, Chan KT. Low-energy design for air-cooled chiller plants in air-conditioned buildings. Energy Build 2006;38(4):334–9.[15] Gang W, Wang S, Xiao F, Gao DC. Robust optimal design of building cooling systems considering cooling load uncertainty and equipment reliability. Appl Energy 2015a;159:265–75.[16] Gang W, Wang S, Yan C, Xiao F. Robust optimal design of building cooling systems concerning uncertainties using mini-max regret theory. Sci Technol Built Environ 2015b;21(6):789–99.[17] Cheng Q, Yan C, Wang S. Robust optimal design of chiller plants based on cooling load distribution. Energy Procedia 2015;75:1354–9.[18] Kang Y, Augenbroe G, Li Q, Wang Q. Effects of scenario uncertainty on chiller sizing method. Appl Therm Eng 2017;123:187–95.[19] Cheng Q, Wang S, Yan C, Xiao F. Probabilistic approach for uncertainty-based optimal design of chiller plants in buildings. Appl Energy 2017;185:1613–24.[20] Huang P, Huang G, Augenbroe G, Li S. Optimal configuration of multiple-chiller plants under cooling load uncertainty for different climate effects and building types. Energy Build 2018;158:684–97.[21] Li H, Wang S, Xiao F. Probabilistic optimal design and on-site adaptive commissioning of building air-conditioning systems concerning uncertainties. Energy Procedia 2019;158:2725–30.[22] Shiming D. Sizing replacement chiller plants2. ASHRAE J 2002;44(6):47.[23] Zheng ZX, Li JQ. Optimal chiller loading by improved invasive weed optimization algorithm for reducing energy consumption. Energy Build 2018;161:80–8.[24] Zheng ZX, Li JQ, Duan PY. Optimal chiller loading by improved artificial fish swarm algorithm for energy saving. Math Comput Simulation 2019;155:227–43.[25] Handbook, ASHRAE. Fundamentals. In: Ventilating and air-conditioning engineers. ASHRAE–American Society of Heating; 2017.[26] Torres YD, Herrera HH, Plasencia MAAG, Novo EP, Cabrera LP, et al. Heating ventilation and air-conditioned configurations for hotels an approach review for the design and exploitation. Energy Rep 2020a;6:487–97.[27] Lapin L. Statistics: Meaning and method. 2nd ed.. Harcourt Brace Jovanovich; 1980.[28] Correia R, Diaz Y. Análisis estadístico de perfiles de demanda térmica (Tesis de Grado), Cuba: Universidad de Cienfuegos.; 2020.[29] Torres YD, Justiz MS, Francisco JL, Lazo DÁ, Torres YM, Guerra MA. Methodology for the preparation and selection of black box mathematical models for the energy simulation of screw type chillers. Ing Mec 2020b;23(3):1–6.[30] Sun Y, Wang S, Xiao F. In situ performance comparison and evaluation of three chiller sequencing control strategies in a super high-rise building. Energy Build 2013;61:333–43.[31] Huang S, Zuo W, Sohn MD. A new method for the optimal chiller sequencing control. In: Proceedings of the 14th conference of IBPSA. Hyderabad, India: 2015. p. 316–23.[32] Chang YC, Lin FA, Lin CH. Optimal chiller sequencing by branch and bound method for saving energy. Energy Convers Manage 2005;46(13–14):2158–72.[33] Tredinnick S. Inside insights-Benefits of economic analyses (part 2): Real-world examples. Dist Energy 2011;97(3):66.[34] CIBSE A. Environmental design. The Chartered Institution of Building Services Engineers London; 2018.[35] Montelier S. Reducción del consumo de energía en instalaciones con sistemas de climatización centralizada Todo Agua a Flujo constante (Tesis Doctoral), Cuba: Universidad de Cienfuegos; 2008.[36] Cuza V. Estudio energético del Sistema de climatización del Hotel Jagua (Tesis de maestría), Cuba: Universidad de Cienfuegos; 2010.[37] Díaz Y, Monteagudo JP, Bravo D. Análisis energético de un sistema híbrido de producción de frío. Ing Energética 2015;36(1):38–49.[38] Díaz Y, Valdivia Y, Monteagudo JP, Miranda Y. Application of building energy simulation in the validation of operational strategies of HVAC systems on a tropical hotel. Ing Mec 2017;20(1):31–8.[39] Valdivia Nodal Y, Díaz Torres Y, Lapido Rodríguez M. Alternativas de producción de agua caliente sanitaria en instalaciones hoteleras con climatización centralizada. Rev Univ Soc 2015;7(3):88–94.[40] Udawatta L, Perera A, Witharana S. Analysis of sensory information for efficient operation of energy management systems in commercial hotels2. Electron J Struct Eng 2010;11:3–120.[41] Yang Z, Ghahramani A, Becerik-Gerber B. Building occupancy diversity and HVAC (heating, ventilation, and air conditioning) system energy efficiency. Energy 2016;109:641–9.[42] Shenzhen Hero-Tech Refrigeration Equipment Co, Ltd. 2020, https://hero-tech.en.made-in-china.com/product-group/LMQJDqIlsgkP/Air -cooled-screw-chiller-catalog-2.html.[43] Cardoso AI, García DM, García JF. Evaluación de la factibilidad económico-financiera del proyecto de inversión. Centro Cultural Julio Antonio Mella. Cienfuegos. Univ Soc 2019;11(5):8–18.[44] Meneses E, Roig A, Paz E, Alonso D, Alvarado J. Factores de emisión de CO, CO2, NOx y SO2 para instalaciones generadoras de electricidad en Cuba. 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