Bibliographic citations
Díaz, P., (2020). Desarrollo de software para optimizar el tiempo empleado en la creación de una configuración para embalajes térmicos para cadena de frío de 2º C A 8º C aplicando inteligencia artificial [Tesis, Universidad Nacional de Ingeniería]. http://hdl.handle.net/20.500.14076/20634
Díaz, P., Desarrollo de software para optimizar el tiempo empleado en la creación de una configuración para embalajes térmicos para cadena de frío de 2º C A 8º C aplicando inteligencia artificial [Tesis]. PE: Universidad Nacional de Ingeniería; 2020. http://hdl.handle.net/20.500.14076/20634
@misc{renati/1749893,
title = "Desarrollo de software para optimizar el tiempo empleado en la creación de una configuración para embalajes térmicos para cadena de frío de 2º C A 8º C aplicando inteligencia artificial",
author = "Díaz Arrosquipa, Pedro José",
publisher = "Universidad Nacional de Ingeniería",
year = "2020"
}
Currently, products that are thermosensitive are manufactured, therefore, they must remain at a certain temperature range, some products must be stored at a temperature below 0°C and others must be between 2°C and 8°C, for that reason, care must be taken in handling, especially during transport, because otherwise these products will be damaged by biodegradation and their quality will be diminished. For this reason, thermal packaging was created, which are containers that carry thermosensitive products, to transport them from one place to another, however, to find a thermal packaging that meets the requested conditions: transport time, range of variation of the environmental temperature during the trip and the volume of the thermosensitive product, it is an arduous work and that expands in many occasions, because each configuration devised must be tested to see the thermal behavior inside the packaging. It is with this objective that the present thesis work arose, to optimize the process of creating thermal packaging configuration for working temperature ranges from 2°C to 8°C. To achieve this goal, the first thing that was done was to know how the dynamic model of thermal packaging is, for this purpose a recurrent artificial neural network was developed whose inputs were the ambient temperature to which the thermal packaging is exposed, the dimensions of the product that will contain the packaging and all the materials used, that network was trained with different tests that have already been performed. When the network was already trained it was time to work with optimization algorithms to find the necessary configuration according to the requested requirements: the ambient temperature, volume of the product to be preserved and the transport time. In order for everything mentioned above to be used, an interface was developed, see Figura 3.28, with the objective that everybody be able to use the software.
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