Neural network models for predicting perishable food temperatures along the supply chain
Neural network models for predicting perishable food temperatures along the supply chain
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DOI：
10.1016/j.biosystemseng.2018.04.016
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发表时间：
2018-07-01
影响因子：
5.1
通讯作者：
Uysal, Ismail
中科院分区：
文献类型：
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作者：
Mercier, Samuel;Uysal, Ismail
为了大规模实施质量驱动的配送，需要使用每次装运的有限数量的温度传感器沿着供应链监测易腐食品沿着的温度。在这项工作中，我们建议利用物理传热模型的理论基础和泛化能力来开发一个灵活的神经网络框架，可以实时预测温度。更具体地说，托盘内的温度分布受到不同的环境温度从一个有效的热传递模型生成，并用于训练神经网络。仿真结果表明，神经网络可以预测托盘内的温度分布，平均误差低于0.5 K，在一个传感器，每托盘的情况下，当传感器是正确的位于托盘内。将温度传感器放置在托盘的拐角处提供了与托盘内的其他位置具有强相关性的高信息内容，以最大化温度估计的准确性。应用集成算子来联合收割机从多个随机种子神经网络的预测提高了高达35%的温度估计的准确性。最后，在训练数据中引入小的高斯噪声是提高神经网络泛化能力的有效方法，并且在存在噪声温度传感器的情况下将温度预测的准确性提高了近45%。(C)2018年IAgRE。由爱思唯尔有限公司出版。保留所有权利。
Monitoring the temperature of perishable food along the supply chain using a limited number of temperature sensors per shipment is required for wide-scale implementation of quality-driven distribution. In this work, we propose to leverage the theoretical foundation and generalisation ability of a physical heat transfer model to develop a flexible neural net framework which can predict temperatures in real-time. More specifically, the temperature distribution inside a pallet subjected to different ambient temperatures are generated from a validated heat transfer model, and used to train a neural network. Simulations show that the neural network can predict the temperature distribution inside a pallet with an average error below 0.5 K in a one-sensor-per-pallet scenario when the sensor is properly located inside the pallet. Placing the temperature sensor at the corner of the pallet provides a high information content with strong correlations to the other locations inside the pallet to maximise the accuracy of the temperature estimates. The application of an ensemble operator to combine the predictions from multiple randomly seeded neural networks improved by up to 35% the accuracy of the temperature estimates. Finally, the introduction of small Gaussian noise in the training data is an efficient approach to improve the generalisation ability of the neural network and improved by nearly 45% the accuracy of the temperature prediction in the presence of noisy temperature sensors. (C) 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.