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谷朊粉颗粒热风干燥动力学模型及水分迁移规律

贲宗友 张季伟 韩动梁 肖茂华 陈坤杰

贲宗友,张季伟,韩动梁,等.谷朊粉颗粒热风干燥动力学模型及水分迁移规律[J].农业工程,2022,12(8):62-67. doi: 10.19998/j.cnki.2095-1795.2022.08.011
引用本文: 贲宗友,张季伟,韩动梁,等.谷朊粉颗粒热风干燥动力学模型及水分迁移规律[J].农业工程,2022,12(8):62-67. doi: 10.19998/j.cnki.2095-1795.2022.08.011
BEN Zongyou,ZHANG Jiwei,HAN Dongliang,et al.Dynamic model and moisture migration law of gluten pellets in hot air drying[J].Agricultural Engineering,2022,12(8):62-67. doi: 10.19998/j.cnki.2095-1795.2022.08.011
Citation: BEN Zongyou,ZHANG Jiwei,HAN Dongliang,et al.Dynamic model and moisture migration law of gluten pellets in hot air drying[J].Agricultural Engineering,2022,12(8):62-67. doi: 10.19998/j.cnki.2095-1795.2022.08.011

谷朊粉颗粒热风干燥动力学模型及水分迁移规律

doi: 10.19998/j.cnki.2095-1795.2022.08.011
基金项目: 江苏省国际科技创新合作项目(BZ2020061)
详细信息
    作者简介:

    贲宗友,博士生,主要从事农产品烘干及设备开发研究 E-mail:ZYBen90@163.com

    陈坤杰,通信作者,教授,博士生导师,主要从事农产品加工与无损检测研究E-mail:kunjiechen@njau.edu.cn

  • 中图分类号: S816.8

Dynamic Model and Moisture Migration Law of Gluten Pellets in Hot Air Drying

  • 摘要:

    为了探究谷朊粉颗粒热风干燥过程中的干燥特性及水分迁移规律,开展了谷朊粉颗粒2因素3水平全因素热风干燥试验,考察不同干燥温度(50、60、70 °C)和颗粒厚度(4.24、9.15、15.52 mm)下的干燥特性,运用低场核磁共振技术分析了干燥过程中的水分迁移规律,并建立干燥动力学模型和水分预测模型。结果表明:谷朊粉颗粒干燥速率和水分比随温度升高而显著降低(P<0.05);有效水分扩散系数随温度升高和颗粒厚度增加而增大。决定系数(R2)、离差平方和(\begin{document}$ {\chi }^{2} $\end{document})、均方根误差(RMSE)计算结果表明,Modified Page薄层干燥模型对谷朊粉颗粒的干燥试验数据具有较高的拟合精度,而且建立了模型参数(kn)与干燥温度(T)、颗粒厚度(H)的回归模型(R2>0.926)。低场核磁共振横向弛豫时间(T2)反演谱显示,随干燥时间的增加,各水分峰面积逐渐减小,而且峰位置逐渐向结合水靠近,并建立了含水率(M)与干燥时间(t)、颗粒厚度(H)、干燥温度(T)、弛豫反演图谱总峰面积(A)之间的回归关系,结果表明预测效果较好(R2=0.933)。研究结果可为谷朊粉颗粒干燥工艺提供参考。

     

  • 图 1  不同温度下谷朊粉颗粒水分比和干燥速率曲线

    Figure 1.  Moisture ratio and drying rate curves of gluten pellets at different temperature

    图 2  Modified Page模型验证

    Figure 2.  Validation of Modified Page model

    图 3  同一时刻不同厚度下谷朊粉颗粒的T2反演谱

    Figure 3.  T2 inversion spectra of gluten pellets with different thicknesses at the same time

    图 4  谷朊粉颗粒烘干过程T2反演谱(60 ,9.15 mm)

    Figure 4.  T2 inversion spectrum of gluten pellets during drying (60 °C,9.15 mm)

    图 5  含水率预测模型验证

    Figure 5.  Validation of moisture content prediction model

    表  1  水分有效扩散系数计算结果

    Table  1.   Calculation results of effective moisture diffusivity

    厚度/
    mm
    干燥
    温度/°C
    拟合方程R2$ {D}_{\mathrm{e}\mathrm{f}\mathrm{f}} $/
    (ms−1
    4.2450lnMR=–0.01537t−0.087220.9301.12e-7
    60lnMR=–0.01995t−0.073120.9691.45e-7
    70lnMR=–0.02541t−0.086250.9421.89e-7
    9.1550lnMR=–0.01135t−0.073870.9723.85e-7
    60lnMR=–0.01267t−0.041910.9934.30e-7
    70lnMR=–0.01415t−0.077850.9774.87e-7
    15.5250lnMR=–0.00423t−0.050320.9744.13e-7
    60lnMR=–0.00469t−0.033130.9834.58e-7
    70lnMR=–0.00534t−0.060120.9855.11e-7
    下载: 导出CSV

    表  2  干燥动力学模型拟合结果

    Table  2.   Fitting results of drying kinetic model

    模型$ {R}^{2} $RMSE$ {\chi }^{2} $
    Lewis0.937~0.9900.0146~0.04342.378×10−4~2.265×10−3
    Modified Page0.992~0.9990.0039~0.01533.613×10−5~3.526×10−4
    Henderson0.940~0.9920.0147~0.04112.687×10−4~2.531×10−3
    Wang0.985~0.9990.0053~0.01903.337×10−5~4.489×10−4
    下载: 导出CSV
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  • 收稿日期:  2022-05-09
  • 修回日期:  2022-07-15
  • 出版日期:  2022-08-20

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