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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Published in:Data in brief 2020-08, Vol.31, p.105726-105726, Article 105726
Main Authors: Ayoola, A.A., Hymore, F.K., Omonhinmin, C.A., Babalola, P.O., Bolujo, E.O., Adeyemi, G.A., Babalola, R., Olafadehan, O.A.
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Language:English
Subjects:
ANN
Biodiesel
Energy
KOH
NaOH
RSM
Waste soybean oil
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cited_by cdi_FETCH-LOGICAL-c494t-ad29cb19cfc22cc0cd6e057196a1884d149f5986841558b6eb233606aa1d156d3
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creator Ayoola, A.A.
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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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subjects ANN
Biodiesel
Energy
KOH
NaOH
RSM
Waste soybean oil
title Data on artificial neural network and response surface methodology analysis of biodiesel production
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