Artificial Neural Network Modeling of Distillers Dried Grains with Solubles (DDGS) Flowability with Varying Process and Storage Parameters

Abstract

Cereal Chem. 88(5):480-489Neural network (NN) modeling techniques were used to predict flowability behavior of distillers dried grains with solubles (DDGS) prepared with varying levels of condensed distillers solubles (10, 15, and 20%, wb), drying temperatures (100, 200, and 300°C), cooling temperatures (-12, 25, and 35°C), and storage times (0 and 1 month). 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 index. Various NN models were developed using multiple input variables in order to predict single-response and multiple-response variables simultaneously. The NN models were compared based on R 2 , mean square error, and coefficient of variation obtained. In order to achieve results with higher R 2 and lower error, the number of neurons in each hidden layer, the step size, the momentum learning rate, and the number of hidden layers were varied. Results indicate that for all the response variables, R 2 > 0.83 was obtained from NN modeling. Compared with our previous studies, NN modeling provided better results than either partial least squares modeling or regression modeling, indicating greater robustness in the NN models. Surface plots based on the predicted values from the NN models yielded process and storage conditions for favorable versus cohesive flow behavior for DDGS. Modeling of DDGS flowability using NN has not been done before, so this work will be a step toward the application of intelligent modeling procedures to this industrial challenge.

A Scientometric Review of Grain Storage Technology in the Past 15 Years (2007–2022) Based on Knowledge Graph and Visualization

“…It can achieve low temperature, low oxygen, and low energy consumption green management of the grain in stock to achieve loss reduction, consumption reduction, and freshness preservation with the Internet of Things + intelligent grain storage technology [ 50 ]. Food damage can even be reduced to near zero if the technology is mature and scientific storage and management practices are used [ 51 ].…”

Modeling Distillers Dried Grains with Solubles (DDGS) Mass Flow Rate as Affected by Drying and Storage Conditions

“…Because this study is a laboratory‐scale design, we have used lower ranges of consolidation pressure and time. Previous studies with 10–12% fat DDGS showed that AoR provided a better model for flowability (Bhadra et al 2011a). HR, Jenike compressibility, GMD, and color also showed significant effects on DDGS flowability (Bhadra et al 2009a, 2009b).…”