Engineering Commons^™

Artificial Neural Network Modeling Of Ddgs Flowability With Varying Process And Storage Parameters, Rumela Bhadra, K. Muthukumarappan, Kurt A. Rosentrater

Kurt A. Rosentrater

Neural Network (NN) modeling techniques were used to predict flowability behavior of distillers dried grains with solubles (DDGS) prepared with varying condensed distillers soluble (10, 15, and 20%, wb), drying temperature (100, 200, and 300°C), cooling temperature (-12, 0, and 35°C) and cooling time (0 and 1 month) levels. Response variables were selected based on our previous research results, and included aerated bulk density, Hausner Ratio, angle of repose, Total Flowability Index, and Jenike Flow Function. Various neural network models were developed using multiple input variables in order to predict single response variables or multiple response variables simultaneously. The NN …