Real-time temperature prediction in a cold supply chain based on Newton's law of cooling
Real-time temperature prediction in a cold supply chain based on Newton's law of cooling
复制标题
基于牛顿冷却定律的冷供应链实时温度预测
DOI：
10.1016/j.dss.2020.113451
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
H. Leopold
中科院分区：
文献类型：
--
作者：
I. Konovalenko;André Ludwig;H. Leopold
许多商品，包括药品，都需要密切的温度监测。这不仅对于遵守法规很重要，而且对于保证使用安全也很重要。在运输过程中控制产品温度是一个特殊的挑战。在冷供应链 (SC) 中，温度由冷藏集装箱维持。然而，很多情况下，例如冷却系统故障，导致环境温度变化，需要尽早发现，防止产品损坏。现有的温度预测方法仅限于环境温度相对稳定的长期预测和/或依赖于已知固定位置的多个传感器。由于需要立即对 SC 进行干预，因此需要一种能够提供有关常规环境温度不稳定（即环境温度在短期内意外变化时）的实时预测的方法。我们提出了一种新颖的方法，基于一组温度稳定性条件和传感器测量误差，将牛顿冷却定律（NLC）的适用性扩展到可变的环境温度。在该方法中，选择表征稳定环境温度并提高预测可靠性的最佳测量数量。我们将改编后的 NLC 与人工神经网络和自回归移动平均模型在偏差预测、预测误差和执行时间方面进行比较。我们基于真实世界数据的评估表明，改编后的 NLC 优于现有的基线方法。与现有的解决方案相比，我们的方法不需要任何有关产品在容器内的定位的知识，进一步增加了其实用价值。
Many goods, including pharmaceuticals, require close temperature monitoring. This is important not only for complying with regulations but also for guaranteeing safety of use. A particular challenge in controlling a product's temperature arises during transportation. In cold supply chains (SCs), temperature is maintained by refrigerated containers. However, many situations, e.g. cooling system failure, lead to ambient temperature changes, and this needs to be detected as early as possible to prevent product damage. Existing approaches to temperature prediction are confined to long-term forecasts with relatively stable ambient temperatures and/or rely on multiple sensors in the known fixed positions. Since interventions in a SC are required immediately, there is a need for methods that provide real-time predictions regarding regular ambient temperature instability, i.e. when the ambient temperature changes unexpectedly in the short term. We propose a novel method that extends the applicability of Newton's law of cooling (NLC) to changeable ambient temperatures based on a set of temperature stability conditions and a sensor measurement error. In the method, an optimal number of measurements that characterize stable ambient temperatures and improve prediction reliability are selected. We compare the adapted NLC with artificial neural networks and autoregressive moving average models with respect to deviation prediction, prediction error, and execution time. Our evaluation based on real-world data shows that the adapted NLC outperforms existing baseline methods. In contrast to existing solutions, our method does not require any knowledge about the positioning of products within the container, further increasing its practical value.
DOI：
10.1145/3360726
发表时间：
2020-01
期刊：
ACM Transactions on Sensor Networks (TOSN)
影响因子：
--
作者：
Amitangshu Pal;K. Kant
通讯作者：
Amitangshu Pal;K. Kant