对混合对流辐射干燥系统进行分析和控制,以实现节能战略
Mohamed Qenawy1, Mona Ali1, Hany S El-Mesery1
1School of Energy and Power Engineering, Jiangsu University, Zhenjiang, China.
Journal of food science
|November 24, 2024
概括
混合对流辐射干燥大片可以节省能源. 一个人工神经网络 (ANN) 模型通过控制辐射强度来优化干燥,将时间缩短6.5%,节省36%的能量.
科学领域:
- 农业工程 农业工程
- 食品科学与技术 食品科学与技术
- 节能 节能 节能 节能 节能
背景情况:
- 食品干燥对于农业保存至关重要,混合干燥提供有效的水分去除.
- 湿度比率和干燥速度的短暂行为可以在没有有效策略的情况下增加能源消耗.
- 优化混合干燥工艺对于食品保存的能源效率至关重要.
研究的目的:
- 研究混合对流辐射干燥对湿度比,干燥速度,时间和能源消耗的影响.
- 为运行条件和干燥动力学之间的关系提供新的见解.
- 应用人工神经网络 (ANN) 模型来优化干燥过程中的能源消耗.
主要方法:
- 进行了混合对流-辐射干燥实验,使用不同的空气流速 (0.71.5 m/s),温度 (4060°C) 和辐射强度 (15003000 W/m2).
- 使用统计方法和自组织地图分析了湿度比率和干燥率的短暂行为.
- 开发并应用ANN控制模型以优化能源消耗,实现99%的预测准确度.
主要成果:
- 增加辐射强度和空气温度减少了干燥时间;较高的空气流增加了它.
- 随着辐射强度和温度的提高,以及空气流速的降低,能源指数得到了改善.
- 通过对ANN模型进行优化,采用60°C的空气和0.7米/秒的空气流量,将干燥时间缩短6.5%,并节省了36%的能源.
结论:
- 混合对流-辐射干燥参数显著影响干燥动力学和能源消耗.
- ANN建模提供了一种有效的方法,用于优化大片干燥中的能源使用.
- 控制辐射强度是提高加工效率和节能食品干燥的关键.
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