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Process modeling and optimization of sorrel biodiesel synthesis using barium hydroxide as a base heterogeneous catalyst: appraisal of response surface methodology, neural network and neuro-fuzzy system

  • Niyi B. Ishola
  • Adebisi A. Okeleye
  • Ajiboye S. Osunleke
  • Eriola BetikuEmail author
Original Article
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Abstract

In this study, three different modeling tools, viz. response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), were used to model the process of conversion of sorrel (Hibiscus sabdariffa) oil to H. sabdariffa methyl esters (HSME). The high free fatty acid (13.47%) of the sorrel oil was reduced to 0.62 ± 0.05% using methanol/oil molar ratio of 40:1, catalyst (ferric sulfate) weight of 15 wt%, reaction time of 3 h and temperature of 65 °C, followed by transesterification step. The developed models for the transesterification process were all found to be reliable and accurate when subjected to different statistical tests. ANFIS model [coefficient of determination (R2) = 0.9944] was better than ANN model (R2 = 0.9875), while RSM model (R2 = 0.9789) was the least accurate. The results of process optimization for the transesterification showed that genetic algorithm (GA) performed better than RSM. The highest HSME yield of 99.71 wt% could be obtained under optimal condition of methanol/oil molar ratio 8:1, catalyst weight 1.23 wt% and reaction time 43 min while keeping temperature at 65 °C using ANFIS model which has been optimized with GA. The sensitivity analyses showed that time was the most important input variable, followed by methanol/oil molar ratio and lastly catalyst weight. Quality characterization of the HSME showed that it could serve as an alternative to petro-diesel.

Keywords

Artificial neural network Adaptive neuro-fuzzy inference system Genetic algorithm Response surface methodology 

Abbreviations

ANFIS

Adaptive neuro-fuzzy inference system

ANN

Artificial neural network

ANOVA

Analysis of variance

CCRD

Central composite rotatable design

CV

Coefficient of variance

FFA

Free fatty acid

FT-IR

Fourier transform infrared

GA

Genetic algorithm

GMF

Gaussian membership function

HSO

Hibiscus sabdariffa oil

HSME

Hibiscus sabdariffa methyl esters

MSE

Mean square error

MAE

Mean absolute error

MRPD

Mean relative percent deviation

R

Correlation coefficient

R2

Coefficient of determination

RMSE

Root mean square error

RSM

Response surface methodology

Notes

Acknowledgements

EB thankfully acknowledged DAAD for provision of relevant literature and equipment provision by World University Service (WUS), Wiesbaden, Germany.

Compliance with ethical standards

Conflict of interest

We declare that there is no conflict of interest.

Supplementary material

521_2018_3989_MOESM1_ESM.docx (290 kb)
Supplementary material 1 (DOCX 290 kb)

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Biochemical Engineering Laboratory, Department of Chemical EngineeringObafemi Awolowo UniversityIle-IfeNigeria
  2. 2.Process Systems Engineering Laboratory, Department of Chemical EngineeringObafemi Awolowo UniversityIle-IfeNigeria

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