Heat Pump Drying Characteristics and Drying Mathematical Model of Chive Flower Sauce

LIU Yu, LI Bao-guo

Packaging Engineering ›› 2022 ›› Issue (5) : 100-107.

PDF(21439 KB)
PDF(21439 KB)
Packaging Engineering ›› 2022 ›› Issue (5) : 100-107. DOI: 10.19554/j.cnki.1001-3563.2022.05.014

Heat Pump Drying Characteristics and Drying Mathematical Model of Chive Flower Sauce

  • LIU Yu, LI Bao-guo
Author information +
History +

Abstract

The work aims to study the heat pump drying characteristics of chive flower sauce, and establish a drying dynamics model. The chive flower sauce was used as the research object and treated by heat pump drying. Then, the effect of different air temperature, wind speed, loading amount and material thickness on the drying time and drying rate of chive flower sauce was studied, and nonlinear regression was carried out to the drying model according to the experimental data. There was no obvious constant speed stage in the heat pump drying process of chive flower sauce. With the increase of air temperature and wind speed, the drying rate increased and the drying time was shortened, but the color and odor of the material were greatly affected, resulting in a decrease in the quality of dry materials. Meanwhile, with the increase of the material thickness, the drying rate was significantly reduced and the drying time was prolonged. Under all experimental conditions, the data predicted by Midilli model fitted well with the experimental data. The drying process of chive flower sauce is affected by the air temperature, wind speed, loading amount and thickness to varying degrees, and the Midilli model is the optimal model to describe the law of moisture change during the drying process of chive flower sauce.

Cite this article

Download Citations
LIU Yu, LI Bao-guo. Heat Pump Drying Characteristics and Drying Mathematical Model of Chive Flower Sauce[J]. Packaging Engineering. 2022(5): 100-107 https://doi.org/10.19554/j.cnki.1001-3563.2022.05.014
PDF(21439 KB)

Accesses

Citation

Detail

Sections
Recommended

/