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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EB thankfully acknowledged DAAD for provision of relevant literature and equipment provision by World University Service (WUS), Wiesbaden, Germany.
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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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DOI: https://doi.org/10.1007/s00521-018-03989-7