Convective drying of fruit: A deeper look at the air-material interface by conjugate modeling

Abstract

A better physical understanding but also prediction of convective drying processes of fruit is essential for further process optimization. This study uses validated conjugate modeling to gain insight in how fruit drying kinetics are related to the convective heat and mass exchange with the surrounding turbulent airflow via the fruit surface. Conjugate modeling implies that the heat and mass transport in both air and fruit domains are solved simultaneously. We explore the impact of several model assumptions and different convective drying conditions. The conjugate model is inherently more accurate than the use of constant convective transfer coefficients (CTCs), so the non-conjugate approach. However the gain in accuracy was found to be limited in terms of overall fruit drying kinetics, such as total mass loss. Nevertheless, conjugate modeling allowed to identify spatial and temporal variability in CTCs, which locally affected drying rates and internal moisture content distribution. Thereby, we identified the occurrence of negative convective transfer coefficients, which led to rehydration at specific locations on the fruit surface, due to the surrounding high-humidity microclimate. The ability to identify the direct relation between non-uniformities in the airflow to those in the tissue is a unique trait of the conjugate approach. Furthermore, it was shown that isothermal modeling should not be used, even for near-isothermal conditions such as low-temperature drying, and that including thermal radiation exchange with the environment clearly affected the drying rates. Regarding the drying conditions, the impact of the air speed and approach flow temperature was found to be smaller compared to altering the approach flow humidity. When direct solar radiation was present, the presence of airflow provided significant cooling of the fruit, which is beneficial for preserving heat-sensitive nutritional compounds in the fruit, and also enhanced the drying rate. This study will aid drying technologists to define the required complexity of their model.

Insights in convective drying of fruit by coupled modeling of fruit drying, deformation, quality evolution and convective exchange with the airflow

“…The differences found in drying kinetics in this study between non-conjugate and semi-conjugate approaches are quite significant. This contrasts the much smaller differences that were observed between a fully-conjugate approach and a non-conjugate approach for 2D drying of a fruit slice [40]. The main reason for this mismatch is attributed to the 2D-3D modeling difference.…”

“…Since the distance for moisture to travel from the fruit center to the side surface is shorter, due to the small thickness of the fruit, the overall drying will be faster for the non-conjugate case. The difference between non-conjugate and semi-conjugate (or fullconjugate) modeling will be less obvious for larger slice thicknesses, and was found to be small for 2D modeling [40].…”

“…Furthermore, such spatially-constant CTCs have often been determined for a different geometry as the one of interest, in this case being a half-circular fruit slice. A fully-conjugate simulation on the other hand, where transport in the fruit and airflow domains are solved simultaneously in a transient way, would significantly increase the calculation time but not necessarily the accuracy, as [40] showed recently for fruit drying. The choice for the semiconjugate modeling in this study is a compromise between accuracy and computational cost for the 3D problem.…”

Comparison between airborne ultrasound and contact ultrasound to intensify air drying of blackberry: Heat and mass transfer simulation, energy consumption and quality evaluation

“…3 b. The decline of temperature in this phenomenon can be attributed to the evaporation of water at blackberry surface [51] .…”

Conveyor-Belt Dryers with Tangential Flow for Food Drying: Development of Drying ODEs Useful to Design and Process Adjustment

“…Many results have emerged from these mathematical modeling over several decades. They were then applied to a multitude of products: madarin [14], apple [15][16][17][18][19], apricot [20], mango [21][22][23][24], coroba [25], pear [26], kiwi [27], papaya [28], coconut [29], sultanas [30], banana [31][32][33], generic fruits [34][35][36][37][38][39], generic foods [40][41][42][43][44][45][46][47][48][49][50][51][52][53].…”