Statistical-mathematical procedure to determine the cooling distribution of a chiller plant
This paper presents a procedure to determine the cooling capacity distribution of the chillers composing a chiller plant using a statistical analysis of the building cooling demand. The mathematical-statistical procedure uses tools such as frequency histograms, box-and-whisker plots, stem-and-leaf p...
| Published in: | Energy Reports |
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| Main Authors: | , , , , , |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
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| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722012872 |
| _version_ | 1852688085639233536 |
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| author | Yamile Díaz Torres Hernán Hernández Herrera Migdalia Torres del Toro Mario A. Álvarez Guerra Paride Gullo Jorge Iván Silva Ortega |
| author_facet | Yamile Díaz Torres Hernán Hernández Herrera Migdalia Torres del Toro Mario A. Álvarez Guerra Paride Gullo Jorge Iván Silva Ortega |
| author_sort | Yamile Díaz Torres |
| collection | DOAJ |
| container_title | Energy Reports |
| description | This paper presents a procedure to determine the cooling capacity distribution of the chillers composing a chiller plant using a statistical analysis of the building cooling demand. The mathematical-statistical procedure uses tools such as frequency histograms, box-and-whisker plots, stem-and-leaf plots, the generalized least squares method, and finally an iterative factorial procedure to generate from the processed information. Besides the manufacturer’s data, all possible chiller plant combinations considering design constraints. The procedure was verified in a hotel facility. Eight thermal demand profiles were simulated. Statistical analysis yielded a range of individual capacities between 100–353 kW. The procedure generated 189 refrigeration plant combinations between 2 to 5 chillers, with a safety factor (SF) between 10%–20%. The highest number of combinations considered plants comprising three and four chillers, reaching 50 and 70 chiller plant options, respectively. |
| format | Article |
| id | doaj-art-be4efa61d8ff4eeeae2aa2f09e1f150d |
| institution | Directory of Open Access Journals |
| issn | 2352-4847 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| spelling | doaj-art-be4efa61d8ff4eeeae2aa2f09e1f150d2025-08-19T21:25:41ZengElsevierEnergy Reports2352-48472022-11-01851252610.1016/j.egyr.2022.07.023Statistical-mathematical procedure to determine the cooling distribution of a chiller plantYamile Díaz Torres0Hernán Hernández Herrera1Migdalia Torres del Toro2Mario A. Álvarez Guerra3Paride Gullo4Jorge Iván Silva Ortega5Departamento de ingeniería y ciencias exactas, Politécnico Superior “Alborecer da Juventude”, Calle 45, Reparto Nova Vida, Luanda, AngolaUniversidad Simón Bolívar, Facultad de Ingenierías, Barranquilla, Colombia; Corresponding author.Departamento de ingeniería y ciencias exactas, Politécnico Superior “Alborecer da Juventude”, Calle 45, Reparto Nova Vida, Luanda, AngolaStudy Center of Energy and Environment, University Carlos Rafael Rodríguez, Cienfuegos, CubaUniversity of Southern Denmark (SDU), Department of Mechanical and Electrical Engineering, Alsion 2, 6400 Sønderborg, DenmarkDepartamento de Energía, Universidad de la Costa, Barranquilla, ColombiaThis paper presents a procedure to determine the cooling capacity distribution of the chillers composing a chiller plant using a statistical analysis of the building cooling demand. The mathematical-statistical procedure uses tools such as frequency histograms, box-and-whisker plots, stem-and-leaf plots, the generalized least squares method, and finally an iterative factorial procedure to generate from the processed information. Besides the manufacturer’s data, all possible chiller plant combinations considering design constraints. The procedure was verified in a hotel facility. Eight thermal demand profiles were simulated. Statistical analysis yielded a range of individual capacities between 100–353 kW. The procedure generated 189 refrigeration plant combinations between 2 to 5 chillers, with a safety factor (SF) between 10%–20%. The highest number of combinations considered plants comprising three and four chillers, reaching 50 and 70 chiller plant options, respectively.http://www.sciencedirect.com/science/article/pii/S2352484722012872Chiller plantsStatistical-mathematical procedureChiller plant combinationsCooling capacityChillers |
| spellingShingle | Yamile Díaz Torres Hernán Hernández Herrera Migdalia Torres del Toro Mario A. Álvarez Guerra Paride Gullo Jorge Iván Silva Ortega Statistical-mathematical procedure to determine the cooling distribution of a chiller plant Chiller plants Statistical-mathematical procedure Chiller plant combinations Cooling capacity Chillers |
| title | Statistical-mathematical procedure to determine the cooling distribution of a chiller plant |
| title_full | Statistical-mathematical procedure to determine the cooling distribution of a chiller plant |
| title_fullStr | Statistical-mathematical procedure to determine the cooling distribution of a chiller plant |
| title_full_unstemmed | Statistical-mathematical procedure to determine the cooling distribution of a chiller plant |
| title_short | Statistical-mathematical procedure to determine the cooling distribution of a chiller plant |
| title_sort | statistical mathematical procedure to determine the cooling distribution of a chiller plant |
| topic | Chiller plants Statistical-mathematical procedure Chiller plant combinations Cooling capacity Chillers |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722012872 |
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