Optimization of Oil Yield from the Macro Algae Spirogyra by Solvent Extraction Process Using RSM and ANN
The present work was done to optimize the process parameters of the oil extraction from the algae species spirogyra by using n-hexane as the solvent using the Soxhlet apparatus. The response surface methodology (RSM) and artificial neural network (ANN) were employed to optimize the particle size of...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2022/3690635 |
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| author | S. Aravind Debabrata Barik Nagaraj Ashok |
| author_facet | S. Aravind Debabrata Barik Nagaraj Ashok |
| author_sort | S. Aravind |
| collection | DOAJ |
| description | The present work was done to optimize the process parameters of the oil extraction from the algae species spirogyra by using n-hexane as the solvent using the Soxhlet apparatus. The response surface methodology (RSM) and artificial neural network (ANN) were employed to optimize the particle size of the algae powder, dryness level of the algae powder, solid to solvent ratio, reaction time, and extraction temperature of the oil extraction process. Also, the physiochemical properties of the extracted oil were investigated. The comparative evaluation was done between the RSM and ANN models to select the more precise and accurate model. The coefficient of determination, R2 of 98.92%, and the mean absolute percentage deviation (MAPD) of 0.492% for ANN revealed that the current model created with a network topology of 3 : 11 : 1 with tansig (hyperbolic tangent sigmoid) transfer function in the input layer and purelin (pure linear) transfer function in the output layer trained with trainlm (Levenberg–Marquardt) algorithm found to provide the optimal solution with better accuracy in prediction of the output. The physicochemical properties investigated, such as heating value, flashpoint, density, viscosity, iodine number, acid value, saponification value, and cetane index, showed that the extracted oil from the algae spirogyra species can be used as an alternative fuel. |
| format | Article |
| id | doaj-art-999054e9150a4e10af6ffc1d4367c314 |
| institution | Kabale University |
| issn | 1687-529X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-999054e9150a4e10af6ffc1d4367c3142025-08-20T03:36:48ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/3690635Optimization of Oil Yield from the Macro Algae Spirogyra by Solvent Extraction Process Using RSM and ANNS. Aravind0Debabrata Barik1Nagaraj Ashok2Research ScholarDepartment of Mechanical EngineeringFaculty of Mechanical EngineeringThe present work was done to optimize the process parameters of the oil extraction from the algae species spirogyra by using n-hexane as the solvent using the Soxhlet apparatus. The response surface methodology (RSM) and artificial neural network (ANN) were employed to optimize the particle size of the algae powder, dryness level of the algae powder, solid to solvent ratio, reaction time, and extraction temperature of the oil extraction process. Also, the physiochemical properties of the extracted oil were investigated. The comparative evaluation was done between the RSM and ANN models to select the more precise and accurate model. The coefficient of determination, R2 of 98.92%, and the mean absolute percentage deviation (MAPD) of 0.492% for ANN revealed that the current model created with a network topology of 3 : 11 : 1 with tansig (hyperbolic tangent sigmoid) transfer function in the input layer and purelin (pure linear) transfer function in the output layer trained with trainlm (Levenberg–Marquardt) algorithm found to provide the optimal solution with better accuracy in prediction of the output. The physicochemical properties investigated, such as heating value, flashpoint, density, viscosity, iodine number, acid value, saponification value, and cetane index, showed that the extracted oil from the algae spirogyra species can be used as an alternative fuel.http://dx.doi.org/10.1155/2022/3690635 |
| spellingShingle | S. Aravind Debabrata Barik Nagaraj Ashok Optimization of Oil Yield from the Macro Algae Spirogyra by Solvent Extraction Process Using RSM and ANN International Journal of Photoenergy |
| title | Optimization of Oil Yield from the Macro Algae Spirogyra by Solvent Extraction Process Using RSM and ANN |
| title_full | Optimization of Oil Yield from the Macro Algae Spirogyra by Solvent Extraction Process Using RSM and ANN |
| title_fullStr | Optimization of Oil Yield from the Macro Algae Spirogyra by Solvent Extraction Process Using RSM and ANN |
| title_full_unstemmed | Optimization of Oil Yield from the Macro Algae Spirogyra by Solvent Extraction Process Using RSM and ANN |
| title_short | Optimization of Oil Yield from the Macro Algae Spirogyra by Solvent Extraction Process Using RSM and ANN |
| title_sort | optimization of oil yield from the macro algae spirogyra by solvent extraction process using rsm and ann |
| url | http://dx.doi.org/10.1155/2022/3690635 |
| work_keys_str_mv | AT saravind optimizationofoilyieldfromthemacroalgaespirogyrabysolventextractionprocessusingrsmandann AT debabratabarik optimizationofoilyieldfromthemacroalgaespirogyrabysolventextractionprocessusingrsmandann AT nagarajashok optimizationofoilyieldfromthemacroalgaespirogyrabysolventextractionprocessusingrsmandann |