Biodiesel has been referred to as a perfect substitute for diesel fuel due to its numerous promising properties. They are renewable, clean, increases energy security, improves the environment and air quality and also provides some good safety benefits. This study is focused on the investigation of the use of natural heterogeneous catalysts for production of biodiesel from jansa seed oil, as well as the implementation of artificial neural network (ANN) for the prediction of biofuel yield and process parameters. The biodiesel was produced through transesterification reaction by reacting jansa seed oil (FFA) with methanol (alcohol) to yield methyl ester. Waste periwinkle shell was prepared in 3 different forms; raw, calcined and acidified. The percentage yield of the methyl ester obtained were calculated and tabulated. The process parameters considered were methanol-oil mole ratio, catalyst concentration, agitation speed, reaction temperature and reaction time. The results of this research work revealed that the calcined periwinkle shell catalyst produced higher yield of biodiesel, compared to the yield obtained from the raw and acidified catalyzed process. The properties of the fatty acid methyl esters were within the standard range. The experimental and predicted yield were marginally the same. Hence, the model accurately predicted the yield with acceptable coefficient of determination and low mean squared error (MSE). The results demonstrate the flexibility of ANN model and the improvement of the model in terms of performance prediction when solving problems with stochastic dataset, especially the transesterification of biodiesel.
| Published in | American Journal of Applied Chemistry (Volume 10, Issue 1) |
| DOI | 10.11648/j.ajac.20221001.12 |
| Page(s) | 7-14 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Jansa Seed, Catalyst, Transesterification, Artificial Neural Network
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APA Style
Chinwe Priscilla Okonkwo, Vincent Ishmael Egbulefu Ajiwe, Ebuka Chidiebere Mmaduakor, Njideka Veronica Nwankwo. (2022). Modelling and Optimization of Biodiesel Production Process Parameters from Jansa Seed Oil (Cussonia bateri) Using Artificial Neural Network. American Journal of Applied Chemistry, 10(1), 7-14. https://doi.org/10.11648/j.ajac.20221001.12
ACS Style
Chinwe Priscilla Okonkwo; Vincent Ishmael Egbulefu Ajiwe; Ebuka Chidiebere Mmaduakor; Njideka Veronica Nwankwo. Modelling and Optimization of Biodiesel Production Process Parameters from Jansa Seed Oil (Cussonia bateri) Using Artificial Neural Network. Am. J. Appl. Chem. 2022, 10(1), 7-14. doi: 10.11648/j.ajac.20221001.12
AMA Style
Chinwe Priscilla Okonkwo, Vincent Ishmael Egbulefu Ajiwe, Ebuka Chidiebere Mmaduakor, Njideka Veronica Nwankwo. Modelling and Optimization of Biodiesel Production Process Parameters from Jansa Seed Oil (Cussonia bateri) Using Artificial Neural Network. Am J Appl Chem. 2022;10(1):7-14. doi: 10.11648/j.ajac.20221001.12
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Download@article{10.11648/j.ajac.20221001.12,
author = {Chinwe Priscilla Okonkwo and Vincent Ishmael Egbulefu Ajiwe and Ebuka Chidiebere Mmaduakor and Njideka Veronica Nwankwo},
title = {Modelling and Optimization of Biodiesel Production Process Parameters from Jansa Seed Oil (Cussonia bateri) Using Artificial Neural Network},
journal = {American Journal of Applied Chemistry},
volume = {10},
number = {1},
pages = {7-14},
doi = {10.11648/j.ajac.20221001.12},
url = {https://doi.org/10.11648/j.ajac.20221001.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajac.20221001.12},
abstract = {Biodiesel has been referred to as a perfect substitute for diesel fuel due to its numerous promising properties. They are renewable, clean, increases energy security, improves the environment and air quality and also provides some good safety benefits. This study is focused on the investigation of the use of natural heterogeneous catalysts for production of biodiesel from jansa seed oil, as well as the implementation of artificial neural network (ANN) for the prediction of biofuel yield and process parameters. The biodiesel was produced through transesterification reaction by reacting jansa seed oil (FFA) with methanol (alcohol) to yield methyl ester. Waste periwinkle shell was prepared in 3 different forms; raw, calcined and acidified. The percentage yield of the methyl ester obtained were calculated and tabulated. The process parameters considered were methanol-oil mole ratio, catalyst concentration, agitation speed, reaction temperature and reaction time. The results of this research work revealed that the calcined periwinkle shell catalyst produced higher yield of biodiesel, compared to the yield obtained from the raw and acidified catalyzed process. The properties of the fatty acid methyl esters were within the standard range. The experimental and predicted yield were marginally the same. Hence, the model accurately predicted the yield with acceptable coefficient of determination and low mean squared error (MSE). The results demonstrate the flexibility of ANN model and the improvement of the model in terms of performance prediction when solving problems with stochastic dataset, especially the transesterification of biodiesel.},
year = {2022}
}
TY - JOUR T1 - Modelling and Optimization of Biodiesel Production Process Parameters from Jansa Seed Oil (Cussonia bateri) Using Artificial Neural Network AU - Chinwe Priscilla Okonkwo AU - Vincent Ishmael Egbulefu Ajiwe AU - Ebuka Chidiebere Mmaduakor AU - Njideka Veronica Nwankwo Y1 - 2022/02/16 PY - 2022 N1 - https://doi.org/10.11648/j.ajac.20221001.12 DO - 10.11648/j.ajac.20221001.12 T2 - American Journal of Applied Chemistry JF - American Journal of Applied Chemistry JO - American Journal of Applied Chemistry SP - 7 EP - 14 PB - Science Publishing Group SN - 2330-8745 UR - https://doi.org/10.11648/j.ajac.20221001.12 AB - Biodiesel has been referred to as a perfect substitute for diesel fuel due to its numerous promising properties. They are renewable, clean, increases energy security, improves the environment and air quality and also provides some good safety benefits. This study is focused on the investigation of the use of natural heterogeneous catalysts for production of biodiesel from jansa seed oil, as well as the implementation of artificial neural network (ANN) for the prediction of biofuel yield and process parameters. The biodiesel was produced through transesterification reaction by reacting jansa seed oil (FFA) with methanol (alcohol) to yield methyl ester. Waste periwinkle shell was prepared in 3 different forms; raw, calcined and acidified. The percentage yield of the methyl ester obtained were calculated and tabulated. The process parameters considered were methanol-oil mole ratio, catalyst concentration, agitation speed, reaction temperature and reaction time. The results of this research work revealed that the calcined periwinkle shell catalyst produced higher yield of biodiesel, compared to the yield obtained from the raw and acidified catalyzed process. The properties of the fatty acid methyl esters were within the standard range. The experimental and predicted yield were marginally the same. Hence, the model accurately predicted the yield with acceptable coefficient of determination and low mean squared error (MSE). The results demonstrate the flexibility of ANN model and the improvement of the model in terms of performance prediction when solving problems with stochastic dataset, especially the transesterification of biodiesel. VL - 10 IS - 1 ER -