基于Arima算法的物联网环境温湿度干燥系统能耗预测

Chi-Phi Do, Quang-Huy Le, Duy-Phuoc Pham, Dinh-Kha Le
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引用次数: 1

摘要

热风循环干燥法具有处理量大的优点。此外,在干燥物料体积较大的干燥室内,空气流速、温度、干燥剂湿度、干燥产品表面积等影响干燥过程的因素总是受到关注。因为这是决定干燥时间和干燥产品质量的因素。然而,干燥时间与干燥系统消耗的能量密切相关。特别是环境的温度和湿度对能耗有很大的影响。本文建立了通用数学模型,利用ARIMA算法对工业干燥系统的能耗进行预测,并应用该数学模型对1000kg /批次的干燥系统进行实际调研,设计了尺寸为3000mm的03个干燥室。× 3000mm × 2500(长×宽×高),总烘干盘面积192m2。能源采用热油炉技术或电阻炉。采集的温湿度数据基于物联网平台。仿真结果表明,温度预测精度为99.09%,湿度预测精度为98.24%,能耗预测精度为96.31%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecast of Energy Consumption of Drying System According to The Environmental Temperature and Humidity on IoT by Arima Algorithm
The hot air recirculating drying method has the advantage of handling large output. Moreover, in the drying chamber with a large volume of drying material, factors affecting the drying process such as air flow rate, temperature, drying agent humidity, and surface area of the drying product are always concerned. Because this is the deciding factor for the drying time as well as the quality of the drying product. However, the drying time is closely related to the energy consumed in the drying system. In particular, the temperature and humidity of the environment have a great influence on energy consumption. This paper has built a general mathematical model, using ARIMA algorithm to predict energy consumption for the industrial drying system and applying the mathematical model to actually survey the drying system with a capacity of 1000 kg /batch, 03 drying chambers are designed with a size of 3000mm. x 3000mm x 2500 (length x width x height), total drying tray area 192 m2. Energy sources use thermal oil furnace technology or resistive furnaces. The collected temperature and humidity data is based on the IoT platform. The simulation results forecast the temperature accurately to 99.09%, the humidity is accurate to 98.24% and the energy consumption reaches 96.31%.
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