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

K-REx Repository

Show simple item record

dc.contributor.author Rosentrater, Kurt A.
dc.contributor.author Muthukumarappan, K.
dc.contributor.author Bhadra, Rumela
dc.date.accessioned 2014-05-30T17:00:55Z
dc.date.available 2014-05-30T17:00:55Z
dc.date.issued 2011-09-01
dc.identifier.uri http://hdl.handle.net/2097/17814
dc.description.abstract Neural 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 R², mean square error, and coefficient of variation obtained. In order to achieve results with higher R² 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, R² > 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. en_US
dc.language.iso en_US en_US
dc.relation.uri https://doi.org/10.1094/CCHEM-12-10-0179
dc.rights This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
dc.rights.uri http://rightsstatements.org/vocab/InC/1.0/
dc.subject Artificial neural network modeling en_US
dc.subject Distillers dried grains with solubles (DDGS) en_US
dc.subject Flowability en_US
dc.subject Process parameters en_US
dc.subject Storage parameters en_US
dc.title Artificial Neural Network Modeling of Distillers Dried Grains with Solubles (DDGS) Flowability with Varying Process and Storage Parameters en_US
dc.type Article (publisher version) en_US
dc.date.published 2011 en_US
dc.citation.doi 10.1094/CCHEM-12-10-0179 en_US
dc.citation.epage 489 en_US
dc.citation.issue 5 en_US
dc.citation.jtitle Cereal Chemistry en_US
dc.citation.spage 480 en_US
dc.citation.volume 88 en_US
dc.contributor.authoreid rbhadra en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). Except where otherwise noted, the use of this item is bound by the following: This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Search K-REx


Advanced Search

Browse

My Account

Statistics








Center for the

Advancement of Digital

Scholarship

cads@k-state.edu