Optimization of Bauhinia monandra seed oil extraction via artificial neural network and response surface methodology: A potential biofuel candidate
Industrial Crops and Products 67 (2015) 387–394
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| Główni autorzy: | , , |
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| Format: | Czasopismo |
| Język: | angielski |
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Elsevier B.V.
2020
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| Dostęp online: | https://ir.oauife.edu.ng/handle/123456789/5074 |
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| _version_ | 1810764568502206464 |
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| author | Aramide M, Akintunde Sheriff Olalekan, Ajala Eriola, Betiku |
| author_facet | Aramide M, Akintunde Sheriff Olalekan, Ajala Eriola, Betiku |
| author_sort | Aramide M, Akintunde |
| collection | DSpace |
| description | Industrial Crops and Products 67 (2015) 387–394 |
| format | Journal |
| id | oai:ir.oauife.edu.ng:123456789-5074 |
| institution | My University |
| language | English |
| publishDate | 2020 |
| publisher | Elsevier B.V. |
| record_format | dspace |
| spelling | oai:ir.oauife.edu.ng:123456789-50742023-05-13T11:12:13Z Optimization of Bauhinia monandra seed oil extraction via artificial neural network and response surface methodology: A potential biofuel candidate Aramide M, Akintunde Sheriff Olalekan, Ajala Eriola, Betiku Oilseed Bauhinia monandra Modeling Optimization Artificial neural network Response surface methodology Industrial Crops and Products 67 (2015) 387–394 The influence of sample weight, time, and solvent type and their reciprocal interactions on Bauhinia monandra seed oil (BMSO) yield using artificial neural network (ANN) and response surface methodology (RSM) was investigated. Also, the BMSO obtained was characterized to determine its aptness for oleochemical industry. Numerically predicted optimum values for the extraction process using RSM model were found to be the same for the developed ANN model. The optimum values were sample weight of 60 g, time of 100 min and petroleum ether with a corresponding BMSO yield of 14.8 wt%. Performance evaluation of the models by multiple coefficient of correlation (R), coefficient of determination (R2) and absolute average deviation (AAD) showed that the ANN model was marginally better (R = 0.9995, R2 = 0.9991, AAD = 0.27%) than the RSM model (R = 0.9993, R2 = 0.9986, AAD = 0.49%) in predicting BMSO yield. Physicochemical properties of the BMSO such as acid value (7.56 mg KOH/g), indicated that it is non-edible and the fatty acids profile showed that the oil was highly unsaturated (87.9%), which makes it a potential candidate for biodiesel production. 2020-01-17T09:42:58Z 2020-01-17T09:42:58Z 2015-01-30 Journal DOI: 10.1016/j.indcrop.2015.01.056 https://ir.oauife.edu.ng/handle/123456789/5074 en application/pdf Elsevier B.V. |
| spellingShingle | Oilseed Bauhinia monandra Modeling Optimization Artificial neural network Response surface methodology Aramide M, Akintunde Sheriff Olalekan, Ajala Eriola, Betiku Optimization of Bauhinia monandra seed oil extraction via artificial neural network and response surface methodology: A potential biofuel candidate |
| title | Optimization of Bauhinia monandra seed oil extraction via artificial neural network and response surface methodology: A potential biofuel candidate |
| title_full | Optimization of Bauhinia monandra seed oil extraction via artificial neural network and response surface methodology: A potential biofuel candidate |
| title_fullStr | Optimization of Bauhinia monandra seed oil extraction via artificial neural network and response surface methodology: A potential biofuel candidate |
| title_full_unstemmed | Optimization of Bauhinia monandra seed oil extraction via artificial neural network and response surface methodology: A potential biofuel candidate |
| title_short | Optimization of Bauhinia monandra seed oil extraction via artificial neural network and response surface methodology: A potential biofuel candidate |
| title_sort | optimization of bauhinia monandra seed oil extraction via artificial neural network and response surface methodology a potential biofuel candidate |
| topic | Oilseed Bauhinia monandra Modeling Optimization Artificial neural network Response surface methodology |
| url | https://ir.oauife.edu.ng/handle/123456789/5074 |
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