Effect of Time and Temperature on Moisture Content, Shrinkage, and Rehydration of Dried Onion
Volume 6, Issue 3, Summer 2009, Pages 57-70
S. Abasi, S. M. Mousavi, M. Mohebi, S. Kiani
Abstract In this paper, the experimental data of the onion drying process by a batch cabinet dryer is investigated. Obtained experimental data including moisture content, shrinkage, and rehydration via random factorial scheme are analyzed. Comparison of data average is carried out with the help of the multi amplitude test of Duncan. Statistical analysis of experimental data shows that time, temperature, and their combined effect have a reasonable impact on the moisture content and rehydration value of dried samples. However, a combined effect of time and temperature on the shrinkage value is not meaningful (P>0.05). The results also show that increasing time and temperature leads to a decrease in the moisture content of the samples, but it increases the value of rehydration and the shrinkage of samples.
Multidimensional Dynamic Modeling of Milk Ultrafiltration Using Neuro-Fuzzy Method and a Hybrid Physical Model
Volume 5, Issue 2, Spring 2008, Pages 3-22
Y. Babazadeh, S. M. Mousavi, M. R. Akbarzadeh
Abstract Prediction of the dynamic crossflow ultrafiltration rate of a protein solution such as milk poses a complex non-linear problem as the filtration rate has a strong dependence on both the solution physicochemical conditions and the operating conditions. As a result, the development of general physics-based models has proved extremely challenging. In this study an alternative dynamic neuro-fuzzy model for milk ultrafiltration that describes the variation in dynamic permeate flux decline with temperature, transmembrane pressure (TMP), fat percentage, pH and molecular weight cut off (MWCO) has been developed with the experimental data of the pilot spiral wound membrane test rig. By increasing the temperature, TMP, and pH the permeate flux is increased, and by increasing fat concentration the permeate flux is decreased. The MWCO variation indicates a paradoxical permeate flux. Additionally, a hybrid physical model for dynamic prediction of total resistance in the milk ultrafiltration by combination of two neuro-fuzzy (ANFIS) models and a physical model (BLA model) is developed. By increasing the TMP and fat concentration, the total resistance is increased. But by increasing the pH and temperature, the total resistance is decreased. Also, MWCO variation indicates a paradoxical total resistance value.