Mathematical modeling and simulation of refractance window drying of mango pulp for moisture, temperature, and heat flux distribution

Abstract This study aims to simulate the coupled heat and mass transfer during refractance window (RW) drying of mango pulp. A simulation model was developed by considering temperature and moisture‐dependent effective diffusivity. With the help of COMSOL Multiphysics, a three‐dimensional (3D) model was developed to solve heat and mass transfer equations. RW drying of mango pulp (6 mm thick) was executed at differe temperature of 75, 80, 85, and 90°C and air velocity of .7 m/s. There was a quick rise in product temperature, and it remained constant throughout the experiment. The drying time required to reduce mango pulp's moisture content from 5.33 to .25 (g water/g dry matter) ranged between 5.7 and 8.5 h during RW drying. Surface moisture was 28, 30, 31, and 33% lower than moisture content at the bottom of pulp after 15 min of drying when drying temperatures were 75, 80, 85, and 90°C, respectively. Diffusivity increases with increasing temperature, and values range from 2.50 × 10 −10 to 3.68 × 10 −10 m 2 /s. Out of six empirical models, page, modified page, and two‐term exponentials were the best fit for drying characteristics. Practical Applications This study explores the heat and mass transfer modeling and simulation of RW drying of mango pulp. The three‐dimensional (3D) model gave a better understanding and visualized the effect of different heat source temperature combinations on temperature profile and their distribution and moisture content and their distribution throughout the sample. The method and mode used for this experiment can enable many researchers to conduct experimental studies on RW drying products. Also, a simulation model is a precise tool for forecasting drying product characteristics, drying behaviors, and physicochemical changes during the drying process. Simulation applications for better designing processes in food industries shall be more in the coming future. The present finding can be used by food processors and researchers for predicting the drying dynamics and subsequent upscaling of the RW drying process at an industrial scale.