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

被引：3

作者：

Ayoola, A. A. . ^{[1
]}

Hymore, F. K. ^{[2
]}

Omonhinmin, C. A. ^{[3
]}

Babalola, P. O. ^{[4
]}

Bolujo, E. O. ^{[5
]}

Adeyemi, G. A. ^{[5
]}

Babalola, R. ^{[6
]}

Olafadehan, O. A. ^{[7
]}

机构：

[1] Covenant Univ, Chem Engn Dept, Ota, Ogun State, Nigeria

[2] Regent Univ, Coll Sci & Technol, Accra, Ghana

[3] Covenant Univ, Biol Sci Dept, Ota, Ogun State, Nigeria

[4] Covenant Univ, Mech Engn Dept, Ota, Ogun State, Nigeria

[5] Covenant Univ, Petr Engn Dept, Ota, Ogun State, Nigeria

[6] Akwa Ibom State Univ, Chem Petrochem Engn Dept, Mkpat Enin, Nigeria

[7] Univ Lagos, Chem & Petr Engn Dept, Lagos, Nigeria

来源：

DATA IN BRIEF | 2020年 / 31卷

关键词：

ANN; Biodiesel; KOH; NaOH; RSM; Waste soybean oil;

D O I：

10.1016/j.dib.2020.105726

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

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 degrees C) and reaction time (50 - 90 min). Also, the usefulness of both the RSM and ANN tools in the accurate prediction of the regression mod els were revealed, with values of R-sq being 0.93 and 0.98 for RSM and ANN respectively. (C) 2020 The Authors. Published by Elsevier Inc.

引用

页数：9

共 10 条

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