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

Data in Brief

F.K. Hymore¹  P.O. Babalola²  A.A. Ayoola³  O.A. Olafadehan⁴  E.O. Bolujo⁵  G.A. Adeyemi⁶  R. Babalola⁶  C.A. Omonhinmin⁷ 

[1] Corresponding author.;Biological Sciences Department, Covenant University, Ota, Ogun State, Nigeria;Chemical Engineering Department, Covenant University, Ota, Ogun State, Nigeria;Chemical/Petrochemical Engineering Department, Akwa Ibom State University, Nigeria;Mechanical Engineering Department, Covenant University, Ota, Ogun State, Nigeria;Petroleum Engineering Department, Covenant University, Ota, Ogun State, Nigeria;Regent University College of Science and Technology, Accra, Ghana;

关键词: ANN;    Biodiesel;    KOH;    NaOH;    RSM;    Waste soybean oil;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown