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  • Author: Ajit K. Mahapatra x
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Prediction of Thermal Properties of Sweet Sorghum Bagasse as a Function of Moisture Content Using Artificial Neural Networks and Regression Models

Abstract

Artificial neural networks (ANN) and traditional regression models were developed for prediction of thermal properties of sweet sorghum bagasse as a function of moisture content and room temperature. Predictions were made for three thermal properties: 1) thermal conductivity, 2) volumetric specific heat, and 3) thermal diffusivity. Each thermal property had five levels of moisture content (8.52%, 12.93%, 18.94%, 24.63%, and 28.62%, w. b.) and room temperature as inputs. Data were sub-partitioned for training, testing, and validation of models. Backpropagation (BP) and Kalman Filter (KF) learning algorithms were employed to develop nonparametric models between input and output data sets. Statistical indices including correlation coefficient (R) between actual and predicted outputs were produced for selecting the suitable models. Prediction plots for thermal properties indicated that the ANN models had better accuracy from unseen patterns as compared to regression models. In general, ANN models were able to strongly generalize and interpolate unseen patterns within the domain of training.

Open access
Flow and Thermal Properties of Stevia Powder

Abstract

Stevia (Stevia rebaudiana Bertoni) has recently received a lot of attention as a sweetener due to its taste and low calorific value. Flow and thermal properties of foods play a significant role in the quantitative analysis of unit operations in the food industry. However, there are no published data available on flow and thermal properties of stevia powder. Powder Flow Tester and KD2 Pro Thermal Properties Analyzer were used to determine the flow and thermal properties of stevia powder, respectively, at different moisture contents (4.96%, 9.68%, 13.99%, 20.08%, and 25.79%, w.b.). Mean angle of internal friction of stevia powder ranged from 41.13° to 46.3°. The mean effective angle of internal friction ranged from 47.8° to 52.5° and the mean flow index ranged from 0.27 to 0.48. Mean thermal conductivity of stevia powder ranged from 0.091 W·m-2·K-1 to 0.115 W·m-2·K-1. Mean thermal diffusivity ranged from 0.103 mm2·s-1 to 0.121 mm2·s-1 and mean volumetric specific heat ranged from 0.865 MJ·m-3·K-1 to 1.019 MJ·m-3·K-1. Polynomial regression models were developed to predict flow and thermal properties of stevia powder using moisture content of stevia powder.

Open access