TY - JOUR
T1 - Semi-empirical thin-layer drying model for the agricultural products
AU - Kumar, Ravi
AU - Kumar, Prashant
AU - Hota, Nishant Kumar
AU - Pandey, Om Prakash
N1 - Publisher Copyright:
© 2024 Taylor & Francis Group, LLC.
PY - 2024
Y1 - 2024
N2 - Drying serves as an effective method to truncate the post-harvest wastage of agricultural products. This research paper presents a new thin-layer drying model to predict drying kinetics in the falling rate region. The semi-empirical model is derived as a modification of Fick’s second law and includes one drying constant. By determining this drying constant at a specific temperature, the model allows for the prediction of drying kinetics at any temperature. A Python program was used to evaluate the drying constant through nonlinear regression. Additionally, the model was simplified to obtain the required drying time at any given temperature. The study examined the drying kinetics of several agricultural products, including potato slices, green peas, and banana slices, at temperatures of 60 °C, 70 °C, and 80 °C, respectively. Experimental data of these agricultural products at various drying temperatures were validated against several known thin-layer drying models and the proposed model. The evaluation of model performance was conducted using statistical measures, specifically R2 and MAE. The higher R2 values (0.991-0.999) and lower MAE values (0.008-0.032) suggest strong agreement between the proposed model and experimental data, demonstrating its efficacy compared to previously reported models. Additionally, the proposed model exhibited the capability to predict drying time with high accuracy (error ± 2.9%) for any given temperature.
AB - Drying serves as an effective method to truncate the post-harvest wastage of agricultural products. This research paper presents a new thin-layer drying model to predict drying kinetics in the falling rate region. The semi-empirical model is derived as a modification of Fick’s second law and includes one drying constant. By determining this drying constant at a specific temperature, the model allows for the prediction of drying kinetics at any temperature. A Python program was used to evaluate the drying constant through nonlinear regression. Additionally, the model was simplified to obtain the required drying time at any given temperature. The study examined the drying kinetics of several agricultural products, including potato slices, green peas, and banana slices, at temperatures of 60 °C, 70 °C, and 80 °C, respectively. Experimental data of these agricultural products at various drying temperatures were validated against several known thin-layer drying models and the proposed model. The evaluation of model performance was conducted using statistical measures, specifically R2 and MAE. The higher R2 values (0.991-0.999) and lower MAE values (0.008-0.032) suggest strong agreement between the proposed model and experimental data, demonstrating its efficacy compared to previously reported models. Additionally, the proposed model exhibited the capability to predict drying time with high accuracy (error ± 2.9%) for any given temperature.
KW - Mathematical model
KW - agricultural products
KW - drying
KW - drying kinetics
KW - python
KW - statistical analysis
UR - http://www.scopus.com/inward/record.url?scp=85210508840&partnerID=8YFLogxK
U2 - 10.1080/00986445.2024.2432672
DO - 10.1080/00986445.2024.2432672
M3 - Article
AN - SCOPUS:85210508840
SN - 0098-6445
JO - Chemical Engineering Communications
JF - Chemical Engineering Communications
ER -