Prediction of mean surface temperature of broiler chicks and load microclimate during transport
Prediction of mean surface temperature of broiler chicks and load microclimate during transport
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DOI：
10.1590/1809-4430-eng.agric.v36n4p593-603/2016
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发表时间：
2016-08-01
期刊：
影响因子：
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通讯作者：
FERNANDES, DANIELLE P. B.
中科院分区：
文献类型：
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作者：
NAZARENO, AÉRICA C.;SILVA, IRAN J. O. DA;FERNANDES, DANIELLE P. B.
本研究旨在利用神经网络建立模型来预测肉鸡运输过程中的平均表面温度和活载小气候条件。这项研究是在巴西圣保罗州进行的，通过使用一辆平均容量为380箱的空调卡车监测9批不同密度的箱子。在每批货物中选择了14个小鸡箱，对每箱5只鸡进行评估。用红外测温仪测量了鸡在装卸过程中的表面平均温度(MST)。通过评估容器的小气候(盒子中央和盒子内部)，记录了气温(T)、相对湿度(RH)和比热容(H)；因此，放置了17个数据记录器，每个盒子(14)一个，沿容器3个。用最小均方(LMS)算法训练的单层七神经元人工神经网络对MST和卡车小气候进行了分析。卸货时的MST对运输过程中的MST有较好的预测作用。在运输过程中，箱子内的小气候条件得到了最好的预测。
This study aimed to determine a model to predict mean surface temperature of broiler chicks and live load microclimate conditions during transport by using neural networks. The research was conducted in the state of Sao Paulo, Brazil, by monitoring nine shipments with different density of boxes using an air-conditioned truck with an average capacity of 380 boxes. Fourteen chick boxes were chosen on each shipment, assessing five chicks per box. The mean surface temperature of chicks (MST) was measured with an infrared thermometer in both loading and unloading. By assessing the container microclimate (center and inside boxes), air temperature (T), relative humidity (RH) and specific enthalpy (h) were recorded; thereby, seventeen data loggers were placed, one per box (14), and three along the container. MST and truck microclimate were analyzed using artificial neural networks with a single layer and seven neurons, which were trained with the least mean square (LMS) algorithm. MST in the unloading showed a better prediction of MST during transport. The best prediction of microclimatic conditions was obtained inside the boxes during the shipment.