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


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.


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



Adaptive neuro-fuzzy inference system


Artificial neural network


Analysis of variance


Central composite rotatable design


Coefficient of variance


Free fatty acid


Fourier transform infrared


Genetic algorithm


Gaussian membership function


Hibiscus sabdariffa oil


Hibiscus sabdariffa methyl esters


Mean square error


Mean absolute error


Mean relative percent deviation


Correlation coefficient


Coefficient of determination


Root mean square error


Response surface methodology



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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