CRYOMOVE: Cold chain real-time management of vaccine delivery using PCM and deep learning

Abstract

Vaccines are temperature-sensitive biological products that can become ineffective or even hazardous if exposed to temperatures outside the recommended range. Therefore, it is crucial to maintain the cold chain during the transportation and storage of vaccines in order to ensure that they arrive at their destination in optimal condition. Vaccines are typically transported and stored in containers lined with Phase Change Material (PCM) to maintain a specific temperature range (usually lower than the ambient temperature). Firstly, we have simulated the melting of PCM at three distinct external temperatures using ANSYS Fluent software in order to forecast the Melt Fraction (MF) vs. time curve for each of these temperatures. The external temperature consistently undergoes dynamic changes. Running a simulation each time the temperature shifts is impractical due to its time and data-intensive nature. Consequently, we adopted a more efficient approach by training a simple Artificial Neural Network (ANN) based on simulation data. This ANN is designed to learn and predict the MF versus time curve for any given external temperature. Real-time temperature data from the vaccine box is transmitted to the cloud. Subsequently, the machine learning model operates on the cloud to predict the remaining time for the PCM to melt. This predictive analysis is performed recursively every 10 min to account for dynamic fluctuations in external temperatures, providing continuous updates on the time remaining for PCM meltdown. This is then conveyed to the cold chain managers to take preventive measures if the external conditions are harsh and there is a chance for vaccines to get ruined because of the temperature. This same approach can be extended to other applications beyond vaccine delivery such as food storage and transportation, pharmaceuticals, and more.