OPTIMIZATION OF OIL EXTRACTION FROM GARCINIA KOLA USING ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGY
The target of this investigation was to model and optimize selected process parameters when extracting oil from Garcinia kola. Artificial neural network (ANN) and Box-Behnken design (BBD) in response surface methodology (RSM) were used for the modelling and optimization of the process parameters. T...
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| Format: | Article |
| Language: | English |
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Alma Mater Publishing House "Vasile Alecsandri" University of Bacau
2020-06-01
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| Series: | Journal of Engineering Studies and Research |
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| Online Access: | https://jesr.ub.ro/index.php/1/article/view/171 |
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| author | SYLVESTER UWADIAE FAITH OVIESU BAMIDELE AYODELE |
| author_facet | SYLVESTER UWADIAE FAITH OVIESU BAMIDELE AYODELE |
| author_sort | SYLVESTER UWADIAE |
| collection | DOAJ |
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The target of this investigation was to model and optimize selected process parameters when extracting oil from Garcinia kola. Artificial neural network (ANN) and Box-Behnken design (BBD) in response surface methodology (RSM) were used for the modelling and optimization of the process parameters. The optimized process values were 397.86 mL and 399.99 mL for solvent volume; 109.32 min and 107.55 min for extraction time; 72.64 g and 70 g for sample mass and maximum yields of 20.839 wt% and 20.488 wt% for RSM and ANN respectively. The highly positively correlated experimental and anticipated values validated the models.
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| format | Article |
| id | doaj-art-2041cb6b3b96424d875c36fbff4bd5a2 |
| institution | Kabale University |
| issn | 2068-7559 2344-4932 |
| language | English |
| publishDate | 2020-06-01 |
| publisher | Alma Mater Publishing House "Vasile Alecsandri" University of Bacau |
| record_format | Article |
| series | Journal of Engineering Studies and Research |
| spelling | doaj-art-2041cb6b3b96424d875c36fbff4bd5a22025-02-09T11:37:38ZengAlma Mater Publishing House "Vasile Alecsandri" University of BacauJournal of Engineering Studies and Research2068-75592344-49322020-06-01262OPTIMIZATION OF OIL EXTRACTION FROM GARCINIA KOLA USING ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGYSYLVESTER UWADIAEFAITH OVIESUBAMIDELE AYODELE The target of this investigation was to model and optimize selected process parameters when extracting oil from Garcinia kola. Artificial neural network (ANN) and Box-Behnken design (BBD) in response surface methodology (RSM) were used for the modelling and optimization of the process parameters. The optimized process values were 397.86 mL and 399.99 mL for solvent volume; 109.32 min and 107.55 min for extraction time; 72.64 g and 70 g for sample mass and maximum yields of 20.839 wt% and 20.488 wt% for RSM and ANN respectively. The highly positively correlated experimental and anticipated values validated the models. https://jesr.ub.ro/index.php/1/article/view/171Garcinia kola, oil extraction, optimization, modeling, RSM, ANN |
| spellingShingle | SYLVESTER UWADIAE FAITH OVIESU BAMIDELE AYODELE OPTIMIZATION OF OIL EXTRACTION FROM GARCINIA KOLA USING ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGY Journal of Engineering Studies and Research Garcinia kola, oil extraction, optimization, modeling, RSM, ANN |
| title | OPTIMIZATION OF OIL EXTRACTION FROM GARCINIA KOLA USING ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGY |
| title_full | OPTIMIZATION OF OIL EXTRACTION FROM GARCINIA KOLA USING ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGY |
| title_fullStr | OPTIMIZATION OF OIL EXTRACTION FROM GARCINIA KOLA USING ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGY |
| title_full_unstemmed | OPTIMIZATION OF OIL EXTRACTION FROM GARCINIA KOLA USING ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGY |
| title_short | OPTIMIZATION OF OIL EXTRACTION FROM GARCINIA KOLA USING ARTIFICIAL NEURAL NETWORK AND RESPONSE SURFACE METHODOLOGY |
| title_sort | optimization of oil extraction from garcinia kola using artificial neural network and response surface methodology |
| topic | Garcinia kola, oil extraction, optimization, modeling, RSM, ANN |
| url | https://jesr.ub.ro/index.php/1/article/view/171 |
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