Thin layer modeling of grated coconut drying
With the increasing demand for new and energy efficient drying methods of agricultural products, various techniques were deviced by researchers around the world. This techniques usually developed in laboratory scale prior to scale-up for actual industrial application. During this stage, modeling usu...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Published: |
Trans Tech Publications Inc
2014
|
| Subjects: | |
| Online Access: | http://dx.doi.org/10.4028/www.scientific.net/AMM.660.367 http://dx.doi.org/10.4028/www.scientific.net/AMM.660.367 http://eprints.uthm.edu.my/6480/1/THIN_LAYER_MODELING_OF_GRATED_COCONUT_DRYING.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | With the increasing demand for new and energy efficient drying methods of agricultural
products, various techniques were deviced by researchers around the world. This techniques usually
developed in laboratory scale prior to scale-up for actual industrial application. During this stage,
modeling usually involved to aid scaling up process. This paper presents the modeling of drying
kinetics of grated coconut using three semi emperical thin layer model. These models were
Logarithmic, Modified Handerson and Pabis, and Verma et al models. Moisture ratio predicted by
these models were compared against experimental drying carried out at four temperatures: 50°C,
60°C, 70°C and 80°C. The performance of these models were analyzed statistically using non-linear
regression using LabFit software. The statistical parameters analyzed were coefficient of
determination (R2), reduced Chi-square (X2), Root Mean Square Error (RMSE) and Residuals.
Higher R2 with lower X2, RMSE and Residuals implies good predictability of the models. From the
analysis, it was found that Logarithmic model yields the best predictive capability of grated
coconut drying kinetics with R2 = 0.9996387, X2 =0.505535x10-3, RMSE = 0.00623597 and
Residuals = 0.0703607. |
|---|