Document
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Parametric optimization of cylindrical vaccine transport containers with dual phase change materials to prevent vaccine freezing and heat damage using artificial neural networks
freetext keyword: Cold chain; Dual PCM; Numerical heat transfer; Sensitivity analysis; Vaccine damage Cold storage; Cylinders (containers); Cold chain; Dual phase; Dual phase change material; Dual phasis; Material thickness; Numerical heat transfer; Phase Change; Second phase; Sensitivity analyzes; Vaccine damage; Sensitivity analysis
related by: authorship_75634
crisid: 12523
doi: 10.1016/j.est.2024.115239
has publication venue: Journal of Energy Storage
date time value: article date 12523
type: Article
abstract: The recent pandemic has accentuated the urgent need for robust solutions to maintain vaccine efficacy during transport within the cold chain infrastructure. This study introduces an optimization methodology for cylindrical vaccine transport containers, integrating a second phase change material (PCM) to improve thermal conditions and prevent vaccine freezing. A parametric study integrated with artificial neural networks (ANNs) was conducted to assess the influence of design parameters, including the thickness of insulation, ice, and second PCM layers, as well as the selection of insulator material and second PCM type, on the safe vaccine storage time (t_s). The results demonstrate that the secondary PCM thickness should have a minimum value of 30 mm to ensure adequate thermal stability. For ice layers exceeding 30 mm, the secondary PCM thickness must be at least 1 mm greater than the ice thickness to maintain an optimal t_s. The parameters with the most significant influence on preventing vaccine freezing were the ice thickness, which contributed up to 83 %25 of variation of t_s, and the second PCM thickness, which contributed up to 73 %25. Moreover, optimizing design parameters resulted in a maximum t_s of 197 h, compared to a minimum value of 5 h, demonstrating the effectiveness of parameter optimization in improving storage performance. These findings provide actionable design guidelines to enhance vaccine transport containers, strengthening cold chain resilience and enabling more effective vaccine distribution.
type: Entity
type: Continuant
type: Thing
artnum: 115239
related by: authorship_75636
has funding vehicle: National Council of Humanities, Sciences, and Technologies of Mexico; Consejo Nacional de Humanidades, Ciencias y Tecnologías, Conahcyt
e iscopus: 2-s2.0-85213841404
type: Document
related by: authorship_75633
most specific type: Document
volume: 109
type: Generically Dependent Continuant
type: Information Content Entity
estado: published
related by: authorship_75635
most specific type: Article

individual
rights: termsOfUse
date: 2026-08-26T04:03:59
publisher: vivo
type: Document
label: RDF description of Parametric optimization of cylindrical vaccine transport containers with dual phase change materials to prevent vaccine freezing and heat damage using artificial neural networks - http://orbis.uaslp.mx/vivo/individual/scopus12522

Article
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Information Content Entity
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National Council of Humanities, Sciences, and Technologies of Mexico; Consejo Nacional de Humanidades, Ciencias y Tecnologías, Conahcyt
type: Relationship
type: BFO_0000020
type: Entity
type: Thing
type: Grant
type: Continuant
funding vehicle for: Parametric optimization of cylindrical vaccine transport containers with dual phase change materials to prevent vaccine freezing and heat damage using artificial neural networks

Journal of Energy Storage
type: Generically Dependent Continuant
type: Information Content Entity
type: Collection
type: Document
type: Continuant
publication venue for: Parametric optimization of cylindrical vaccine transport containers with dual phase change materials to prevent vaccine freezing and heat damage using artificial neural networks
type: Journal
type: Entity
type: Thing
type: Periodical