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Generically Dependent Continuant
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
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.
Parametric optimization of cylindrical vaccine transport containers with dual phase change materials to prevent vaccine freezing and heat damage using artificial neural networks
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
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Information Content Entity
National Council of Humanities, Sciences, and Technologies of Mexico; Consejo Nacional de Humanidades, Ciencias y Tecnologías, Conahcyt