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Artificial Neural Network Modeling of DDGS Flowability with Varying Process and Storage Parameters

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Paper number  MBSK 10-106,  ASABE/CSBE North Central Intersectional Meeting. (doi: 10.13031/2013.36278) @2010
Authors:   Rumela Bhadra, K Muthukumarappan, Kurt A Rosentrater
Keywords:   Hidden layers, Models, Neurons, Neural, Variables

Neural Network (NN) modeling techniques were used to predict flowability behavior in distillers dried grains with solubles (DDGS) prepared with varying CDS (10, 15, and 20%, wb), drying temperature (100, 200, and 300C), cooling temperature (-12, 0, and 35C) 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 models were compared based on R2, mean square error (MSE), and coefficient of variation (% CV) obtained. In order to achieve results with higher R2 and lower error, the number of neurons in each hidden layer, step size, momentum learning rate, and number of hidden layers were varied. Results indicate that for all the response variables, R2 >0.83 was obtained from NN modeling. NN modeling provided better models than PLS modeling procedures (Bhadra et al., 2010c). Also, the best NN models fitted fairly well (R2 >0.63) with the dataset of Ganesan et al (2007), indicating higher robustness in the proposed NN models. Finally, based on the predicted values (from NN modeling) surface plots yielded process and storage conditions for favorable vs. cohesive flow behavior in DDGS. Modeling of DDGS flowability using NN has not been previously done, and hence this work will be a step towards application of intelligent modeling procedures to this industrial challenge.

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