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dc.contributor.authorTehlah, N.*
dc.contributor.authorKaewpradit, P.*
dc.contributor.authorMujtaba, Iqbal*
dc.date.accessioned2016-08-31T14:40:43Z
dc.date.available2016-08-31T14:40:43Z
dc.date.issued2016-12-05
dc.identifier.citationTehlah N, Kaewpradit P and Mujtaba IM (2016) Artificial neural network based modelling and optimization of refined palm oil process. Neurocomputing. 216: 489-501.en_US
dc.identifier.urihttp://hdl.handle.net/10454/8866
dc.descriptionYesen_US
dc.description.abstractThe content and concentration of beta-carotene, tocopherol and free fatty acid is one of the important parameters that affect the quality of edible oil. In simulation based studies for refined palm oil process, three variables are usually used as input parameters which are feed flow rate (F), column temperature (T) and pressure (P). These parameters influence the output concentration of beta-carotene, tocopherol and free fatty acid. In this work, we develop 2 different ANN models; the first ANN model based on 3 inputs (F, T, P) and the second model based on 2 inputs (T and P). Artificial neural network (ANN) models are set up to describe the simulation. Feed forward back propagation neural networks are designed using different architecture in MATLAB toolbox. The effects of numbers for neurons and layers are examined. The correlation coefficient for this study is greater than 0.99; it is in good agreement during training and testing the models. Moreover, it is found that ANN can model the process accurately, and is able to predict the model outputs very close to those predicted by ASPEN HYSYS simulator for refined palm oil process. Optimization of the refined palm oil process is performed using ANN based model to maximize the concentration of beta-carotene and tocopherol at residue and free fatty acid at distillate.en_US
dc.language.isoenen_US
dc.rights© 2016 Elsevier B.V. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (http://creativecommons.org/licenses/by-nc-nd/4.0/)en_US
dc.subjectArtificial neural network; Refined palm oil process; Falling film molecular distillation; Modelling; Optimisationen_US
dc.titleArtificial neural network based modelling and optimization of refined palm oil processen_US
dc.status.refereedYesen_US
dc.typeArticleen_US
dc.type.versionAccepted manuscripten_US
dc.identifier.doihttps://doi.org/10.1016/j.neucom.2016.07.050
refterms.dateFOA2018-07-25T11:09:59Z
dc.date.accepted2016-07-28


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