CRYOMOVE: cold chain real-time management of vaccine delivery using PCM and deep learning
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...
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sg-ntu-dr.10356-1807592024-10-23T01:57:51Z CRYOMOVE: cold chain real-time management of vaccine delivery using PCM and deep learning Bhatt, Tapasvi Baser, Manit Tyagi, Abhishek Ng, Eddie Yin Kwee School of Mechanical and Aerospace Engineering Engineering Deep learning Healthcare 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. 2024-10-23T01:57:51Z 2024-10-23T01:57:51Z 2024 Journal Article Bhatt, T., Baser, M., Tyagi, A. & Ng, E. Y. K. (2024). CRYOMOVE: cold chain real-time management of vaccine delivery using PCM and deep learning. Applied Thermal Engineering, 255, 123962-. https://dx.doi.org/10.1016/j.applthermaleng.2024.123962 1359-4311 https://hdl.handle.net/10356/180759 10.1016/j.applthermaleng.2024.123962 2-s2.0-85199257790 255 123962 en Applied Thermal Engineering © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. |
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Engineering Deep learning Healthcare Bhatt, Tapasvi Baser, Manit Tyagi, Abhishek Ng, Eddie Yin Kwee CRYOMOVE: cold chain real-time management of vaccine delivery using PCM and deep learning |
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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. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Bhatt, Tapasvi Baser, Manit Tyagi, Abhishek Ng, Eddie Yin Kwee |
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Article |
author |
Bhatt, Tapasvi Baser, Manit Tyagi, Abhishek Ng, Eddie Yin Kwee |
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Bhatt, Tapasvi |
title |
CRYOMOVE: cold chain real-time management of vaccine delivery using PCM and deep learning |
title_short |
CRYOMOVE: cold chain real-time management of vaccine delivery using PCM and deep learning |
title_full |
CRYOMOVE: cold chain real-time management of vaccine delivery using PCM and deep learning |
title_fullStr |
CRYOMOVE: cold chain real-time management of vaccine delivery using PCM and deep learning |
title_full_unstemmed |
CRYOMOVE: cold chain real-time management of vaccine delivery using PCM and deep learning |
title_sort |
cryomove: cold chain real-time management of vaccine delivery using pcm and deep learning |
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2024 |
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https://hdl.handle.net/10356/180759 |
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1814777793818394624 |