Optimization of sorrel oil biodiesel production by base heterogeneous catalyst from kola nut pod husk: Neural intelligence‐genetic algorithm versus neuro‐fuzzy‐genetic algorithm
The conversion of sorrel oil to biodiesel through transesterification was conducted in the presence of calcined kola nut husk pod ash as a base heterogeneous catalyst. Thus, to predict the biodiesel production yield, two models based on neural intelligence and neuro‐fuzzy techniques were established...
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| Published in | Environmental progress & sustainable energy Vol. 39; no. 4 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
John Wiley & Sons, Inc
01.07.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1944-7442 1944-7450 |
| DOI | 10.1002/ep.13393 |
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| Abstract | The conversion of sorrel oil to biodiesel through transesterification was conducted in the presence of calcined kola nut husk pod ash as a base heterogeneous catalyst. Thus, to predict the biodiesel production yield, two models based on neural intelligence and neuro‐fuzzy techniques were established. The predictive capability and accuracy of the models were compared using various statistics. The neuro‐fuzzy‐based model (adaptive neuro‐fuzzy inference system, ANFIS) obtained for the transesterification process had a lower (0.32%) mean relative percent deviation (MRPD) and a higher coefficient of determination—R2 (0.9991) compared to the neural intelligence‐based model (artificial neural network, ANN) with MRPD of 0.42% and R2 of 0.9971. Also, the models developed were coupled with genetic algorithm (GA) in order to maximize the sorrel oil biodiesel (SOB) yield at optimum values of the process input parameters. SOB yield of >99.0 wt% was obtained when both developed models were subjected to optimization. The results of the process modeling confirm that neuro‐fuzzy model performed slightly better than neural intelligence model. The sensitivity analysis performed on both models shows that reaction time was the most important input variable while other input variables could not be neglected. The characteristics of the synthesized SOB demonstrate that it satisfied the biodiesel standard limits. |
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| AbstractList | The conversion of sorrel oil to biodiesel through transesterification was conducted in the presence of calcined kola nut husk pod ash as a base heterogeneous catalyst. Thus, to predict the biodiesel production yield, two models based on neural intelligence and neuro‐fuzzy techniques were established. The predictive capability and accuracy of the models were compared using various statistics. The neuro‐fuzzy‐based model (adaptive neuro‐fuzzy inference system, ANFIS) obtained for the transesterification process had a lower (0.32%) mean relative percent deviation (MRPD) and a higher coefficient of determination— R 2 (0.9991) compared to the neural intelligence‐based model (artificial neural network, ANN) with MRPD of 0.42% and R 2 of 0.9971. Also, the models developed were coupled with genetic algorithm (GA) in order to maximize the sorrel oil biodiesel (SOB) yield at optimum values of the process input parameters. SOB yield of >99.0 wt% was obtained when both developed models were subjected to optimization. The results of the process modeling confirm that neuro‐fuzzy model performed slightly better than neural intelligence model. The sensitivity analysis performed on both models shows that reaction time was the most important input variable while other input variables could not be neglected. The characteristics of the synthesized SOB demonstrate that it satisfied the biodiesel standard limits. The conversion of sorrel oil to biodiesel through transesterification was conducted in the presence of calcined kola nut husk pod ash as a base heterogeneous catalyst. Thus, to predict the biodiesel production yield, two models based on neural intelligence and neuro‐fuzzy techniques were established. The predictive capability and accuracy of the models were compared using various statistics. The neuro‐fuzzy‐based model (adaptive neuro‐fuzzy inference system, ANFIS) obtained for the transesterification process had a lower (0.32%) mean relative percent deviation (MRPD) and a higher coefficient of determination—R2 (0.9991) compared to the neural intelligence‐based model (artificial neural network, ANN) with MRPD of 0.42% and R2 of 0.9971. Also, the models developed were coupled with genetic algorithm (GA) in order to maximize the sorrel oil biodiesel (SOB) yield at optimum values of the process input parameters. SOB yield of >99.0 wt% was obtained when both developed models were subjected to optimization. The results of the process modeling confirm that neuro‐fuzzy model performed slightly better than neural intelligence model. The sensitivity analysis performed on both models shows that reaction time was the most important input variable while other input variables could not be neglected. The characteristics of the synthesized SOB demonstrate that it satisfied the biodiesel standard limits. |
| Author | Betiku, Eriola Ishola, Niyi B. |
| Author_xml | – sequence: 1 givenname: Eriola orcidid: 0000-0003-4521-1277 surname: Betiku fullname: Betiku, Eriola email: ebetiku@oauife.edu.ng, ebetiku@yahoo.com organization: Obafemi Awolowo University – sequence: 2 givenname: Niyi B. surname: Ishola fullname: Ishola, Niyi B. organization: Obafemi Awolowo University |
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| Title | Optimization of sorrel oil biodiesel production by base heterogeneous catalyst from kola nut pod husk: Neural intelligence‐genetic algorithm versus neuro‐fuzzy‐genetic algorithm |
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