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Data on artificial neural network and response surface methodology analysis of biodiesel production

The biodiesel production from waste soybean oil (using NaOH and KOH catalysts independently) was investigated in this study. The use of optimization tools (artificial neural network, ANN, and response surface methodology, RSM) for the modelling of the relationship between biodiesel yield and process...

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Autores principales: Ayoola, A.A., Hymore, F.K., Omonhinmin, C.A., Babalola, P.O., Bolujo, E.O., Adeyemi, G.A., Babalola, R., Olafadehan, O.A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7251367/
https://www.ncbi.nlm.nih.gov/pubmed/32478158
http://dx.doi.org/10.1016/j.dib.2020.105726
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author Ayoola, A.A.
Hymore, F.K.
Omonhinmin, C.A.
Babalola, P.O.
Bolujo, E.O.
Adeyemi, G.A.
Babalola, R.
Olafadehan, O.A.
author_facet Ayoola, A.A.
Hymore, F.K.
Omonhinmin, C.A.
Babalola, P.O.
Bolujo, E.O.
Adeyemi, G.A.
Babalola, R.
Olafadehan, O.A.
author_sort Ayoola, A.A.
collection PubMed
description The biodiesel production from waste soybean oil (using NaOH and KOH catalysts independently) was investigated in this study. The use of optimization tools (artificial neural network, ANN, and response surface methodology, RSM) for the modelling of the relationship between biodiesel yield and process parameters was carried out. The variables employed in the experimental design of biodiesel yields were methanol-oil mole ratio (6 – 12), catalyst concentration (0.7 – 1.7 wt/wt%), reaction temperature (48 – 62°C) and reaction time (50 – 90 min). Also, the usefulness of both the RSM and ANN tools in the accurate prediction of the regression models were revealed, with values of R-sq being 0.93 and 0.98 for RSM and ANN respectively.
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spelling pubmed-72513672020-05-29 Data on artificial neural network and response surface methodology analysis of biodiesel production Ayoola, A.A. Hymore, F.K. Omonhinmin, C.A. Babalola, P.O. Bolujo, E.O. Adeyemi, G.A. Babalola, R. Olafadehan, O.A. Data Brief Energy The biodiesel production from waste soybean oil (using NaOH and KOH catalysts independently) was investigated in this study. The use of optimization tools (artificial neural network, ANN, and response surface methodology, RSM) for the modelling of the relationship between biodiesel yield and process parameters was carried out. The variables employed in the experimental design of biodiesel yields were methanol-oil mole ratio (6 – 12), catalyst concentration (0.7 – 1.7 wt/wt%), reaction temperature (48 – 62°C) and reaction time (50 – 90 min). Also, the usefulness of both the RSM and ANN tools in the accurate prediction of the regression models were revealed, with values of R-sq being 0.93 and 0.98 for RSM and ANN respectively. Elsevier 2020-05-20 /pmc/articles/PMC7251367/ /pubmed/32478158 http://dx.doi.org/10.1016/j.dib.2020.105726 Text en © 2020 The Authors. Published by Elsevier Inc. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Energy
Ayoola, A.A.
Hymore, F.K.
Omonhinmin, C.A.
Babalola, P.O.
Bolujo, E.O.
Adeyemi, G.A.
Babalola, R.
Olafadehan, O.A.
Data on artificial neural network and response surface methodology analysis of biodiesel production
title Data on artificial neural network and response surface methodology analysis of biodiesel production
title_full Data on artificial neural network and response surface methodology analysis of biodiesel production
title_fullStr Data on artificial neural network and response surface methodology analysis of biodiesel production
title_full_unstemmed Data on artificial neural network and response surface methodology analysis of biodiesel production
title_short Data on artificial neural network and response surface methodology analysis of biodiesel production
title_sort data on artificial neural network and response surface methodology analysis of biodiesel production
topic Energy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7251367/
https://www.ncbi.nlm.nih.gov/pubmed/32478158
http://dx.doi.org/10.1016/j.dib.2020.105726
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