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

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

1.
南京农业大学工学院，江苏南京 210031
2.
滁州学院生物与食品工程学院，安徽滁州 239000
3.
江苏丰尚智能科技有限公司，江苏扬州 225127

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
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- 被引次数: 0
出版历程
- 收稿日期: 2022-05-09
- 修回日期: 2022-07-15
- 出版日期: 2022-08-20

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

1.
College of Engineering，Nanjing Agricultural University，Nanjing Jiangsu 210031，China
2.
College of Biotechnology and Food Engineering，Chuzhou University，Chuzhou Anhui 239000，China
3.
Jiangsu Fengshang Intelligent Technology Co.，Ltd.，Yangzhou Jiangsu 225127，China

摘要

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} $\end{document}）、均方根误差（RMSE）计算结果表明，Modified Page薄层干燥模型对谷朊粉颗粒的干燥试验数据具有较高的拟合精度，而且建立了模型参数（k、n）与干燥温度（T）、颗粒厚度（H）的回归模型（R²＞0.926）。低场核磁共振横向弛豫时间（T₂）反演谱显示，随干燥时间的增加，各水分峰面积逐渐减小，而且峰位置逐渐向结合水靠近，并建立了含水率（M）与干燥时间（t）、颗粒厚度（H）、干燥温度（T）、弛豫反演图谱总峰面积（A）之间的回归关系，结果表明预测效果较好（R²=0.933）。研究结果可为谷朊粉颗粒干燥工艺提供参考。
- 谷朊粉颗粒 /
- 热风干燥 /
- 动力学模型 /
- 低场核磁共振 /
- 水分迁移规律
Abstract:
A two-factor three-level full-factor hot air drying experiment was carried out at different drying temperatures（50, 60, 70 °C）and pellet thicknesses（4.24, 9.15, 15.52 mm）, in order to explore drying characteristics and moisture migration law of gluten pellets． Low-field nuclear magnetic resonance technology was used to analyze moisture migration law in drying process, and a drying kinetic model and moisture prediction model was established． Rresults showed that drying rate and moisture ratio of gluten pellets decreased significantly with increase of temperature（P<0.05）． Effective water diffusion coefficient increaseed with temperature and pellet thickness raising． Calculation results of determination coefficient（R²）, sum of squares of deviation（\begin{document}$ {\chi }^{2} $\end{document}）and root mean square error（RMSE）show that Modified Page thin layer drying model had high fitting accuracy to drying test data of gluten pellets． And regression model of model parameters（k,n）, drying temperature（T）and pellet thickness（H）was established（R²＞0.926）． Low-field nuclear magnetic resonance transverse relaxation time（T₂）inversion spectrum showed that peak area of each moisture gradually decreased, and peak position gradually approached bound water with increase of drying time． Relationship between moisture content（M）and drying time（t）, pellet thickness（H）, drying temperature（T）, total peak area（A）of relaxation inversion spectrum was established． Results showed that prediction accuracy was high（R²=0.933）． Research results could provide reference for drying process of gluten pellets．
- gluten pellets /
- hot air drying /
- dynamic model /
- low-field nuclear magnetic resonance /
- moisture migration law

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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

下载: 全尺寸图片幻灯片

图 2 Modified Page模型验证

Figure 2. Validation of Modified Page model

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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）

下载: 全尺寸图片幻灯片

图 5 含水率预测模型验证

Figure 5. Validation of moisture content prediction model

下载: 全尺寸图片幻灯片

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}} $/ （m^2·s⁻¹）
4.24	50	lnMR=–0.01537t−0.08722	0.930	1.12e-7
	60	lnMR=–0.01995t−0.07312	0.969	1.45e-7
	70	lnMR=–0.02541t−0.08625	0.942	1.89e-7
9.15	50	lnMR=–0.01135t−0.07387	0.972	3.85e-7
	60	lnMR=–0.01267t−0.04191	0.993	4.30e-7
	70	lnMR=–0.01415t−0.07785	0.977	4.87e-7
15.52	50	lnMR=–0.00423t−0.05032	0.974	4.13e-7
	60	lnMR=–0.00469t−0.03313	0.983	4.58e-7
	70	lnMR=–0.00534t−0.06012	0.985	5.11e-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} $
Lewis	0.937～0.990	0.0146～0.0434	2.378×10⁻⁴～2.265×10⁻³
Modified Page	0.992～0.999	0.0039～0.0153	3.613×10⁻⁵～3.526×10⁻⁴
Henderson	0.940～0.992	0.0147～0.0411	2.687×10⁻⁴～2.531×10⁻³
Wang	0.985～0.999	0.0053～0.0190	3.337×10⁻⁵～4.489×10⁻⁴

下载: 导出CSV

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