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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

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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.

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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

R 2 :

Coefficient of determination

RMSE:

Root mean square error

RSM:

Response surface methodology

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Acknowledgements

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

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Authors and Affiliations

  1. Biochemical Engineering Laboratory, Department of Chemical Engineering, Obafemi Awolowo University, Ile-Ife, 220005, Osun State, Nigeria

    Niyi B. Ishola, Adebisi A. Okeleye & Eriola Betiku

  2. Process Systems Engineering Laboratory, Department of Chemical Engineering, Obafemi Awolowo University, Ile-Ife, 220005, Osun State, Nigeria

    Ajiboye S. Osunleke

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  1. Niyi B. Ishola
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Ishola, N.B., Okeleye, A.A., Osunleke, A.S. et al. 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. Neural Comput & Applic 31, 4929–4943 (2019). https://doi.org/10.1007/s00521-018-03989-7

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