Drying Process of Senna alata Medicinal Leave: Comparative Empirical and Artificial Neural Networks Modelling of Mass Transfer Kinetics with Energy Analysis

Abstract

This study investigated the microwave drying of Senna alata leaves (SAL) for sustainable utilization. The effect of SAL form (un-chopped and chopped) and microwave power (200, 400 and 600 W) on the drying characteristics and energy utilization with comparative semi-empirical and artificial neural network (ANN) modelling was investigated. SAL was dried at the selected drying factors (leaf form and microwave power); and moisture transport characteristics including moisture content, moisture ratio, effective moisture diffusivity, activation energy, energy consumption, specific energy consumption and energy efficiency were determined gravimetrically and empirically. In addition, models were utilized to represent the experimental observations and compared statistically. Results showed that un-chopped SAL had a drying time of 10, 8.87, 7.34 s while chopped SAL had a drying time of 8.34, 5.45, 3.5 s at 200, 400 and 600 W, respectively. The effective moisture diffusivity of un-chopped and chopped SAL ranged between 1.40e-6 - 1.94e-6 m2/s and 1.99e-6 – 3.79e-6 m2/s at 200, 400 and 600 W, respectively; while activation energy was 1.79 and 3.64 W/g, respectively. The un-chopped SAL has energy efficiency of 47.38, 26.71 and 21.52% while chopped SAL has energy efficiency of 56.47, 43.49 and 45.14 KJ/kWs at 200, 400 and 600 W. The range of coefficient of determination (R2) of empirical models was 0.9963 – 0.9994 while R2 value of ANN model was 0.9996. It was generally observed that the form of SAL and selected microwave power affected the drying and energy indicators, where size alteration (chopping) and increment in microwave power reduced the drying time and improved the energy indicators. The semi-empirical and ANN models performed well in representing the drying process with ANN having a marginal edge. These results are useful in conservation of SAL, control and commercialization of the microwave drying process.

Keywords

• Senna alata Leave
• Microwave Drying
• Empirical Model
• Artificial Neural Network Model
• Energy Study