Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines
The ever increasing fuel demands and the limitations of oil reserves have motivated research of renewable and sustainable energy resources to replace, even partially, fossil fuels, which are having a serious environmental impact on global warming and climate change, excessive greenhouse emissions an...
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MDPI AG
2018-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/11/11/2995 |
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| author | Marina Corral Bobadilla Roberto Fernández Martínez Rubén Lostado Lorza Fátima Somovilla Gómez Eliseo P. Vergara González |
| author_facet | Marina Corral Bobadilla Roberto Fernández Martínez Rubén Lostado Lorza Fátima Somovilla Gómez Eliseo P. Vergara González |
| author_sort | Marina Corral Bobadilla |
| collection | DOAJ |
| description | The ever increasing fuel demands and the limitations of oil reserves have motivated research of renewable and sustainable energy resources to replace, even partially, fossil fuels, which are having a serious environmental impact on global warming and climate change, excessive greenhouse emissions and deforestation. For this reason, an alternative, renewable and biodegradable combustible like biodiesel is necessary. For this purpose, waste cooking oil is a potential replacement for vegetable oils in the production of biodiesel. Direct transesterification of vegetable oils was undertaken to synthesize the biodiesel. Several variables controlled the process. The alkaline catalyst that is used, typically sodium hydroxide (NaOH) or potassium hydroxide (KOH), increases the solubility and speeds up the reaction. Therefore, the methodology that this study suggests for improving the biodiesel production is based on computing techniques for prediction and optimization of these process dimensions. The method builds and selects a group of regression models that predict several properties of biodiesel samples (viscosity turbidity, density, high heating value and yield) based on various attributes of the transesterification process (dosage of catalyst, molar ratio, mixing speed, mixing time, temperature, humidity and impurities). In order to develop it, a Box-Behnken type of Design of Experiment (DoE) was designed that considered the variables that were previously mentioned. Then, using this DoE, biodiesel production features were decided by conducting lab experiments to complete a dataset with real production properties. Subsequently, using this dataset, a group of regression models—linear regression and support vector machines (using linear kernel, polynomial kernel and radial basic function kernel)—were constructed to predict the studied properties of biodiesel and to obtain a better understanding of the process. Finally, several biodiesel optimization scenarios were reached through the application of genetic algorithms to the regression models obtained with greater precision. In this way, it was possible to identify the best combinations of variables, both independent and dependent. These scenarios were based mainly on a desire to improve the biodiesel yield by obtaining a higher heating value, while decreasing the viscosity, density and turbidity. These conditions were achieved when the dosage of catalyst was approximately 1 wt %. |
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| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T12:56:07Z |
| publishDate | 2018-11-01 |
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| series | Energies |
| spelling | doaj.art-7d5074790b7b486c96487e302f8a92632022-12-22T04:23:04ZengMDPI AGEnergies1996-10732018-11-011111299510.3390/en11112995en11112995Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector MachinesMarina Corral Bobadilla0Roberto Fernández Martínez1Rubén Lostado Lorza2Fátima Somovilla Gómez3Eliseo P. Vergara González4Department of Mechanical Engineering, University of La Rioja, 26004 Logroño, La Rioja, SpainDepartment of Electrical Engineering, University of The Basque Country UPV/EHU, 48013 Bilbao, Biscay, SpainDepartment of Mechanical Engineering, University of La Rioja, 26004 Logroño, La Rioja, SpainDepartment of Mechanical Engineering, University of La Rioja, 26004 Logroño, La Rioja, SpainDepartment of Mining Exploitation and Prospecting, University of Oviedo, 33004 Oviedo, Asturias, SpainThe ever increasing fuel demands and the limitations of oil reserves have motivated research of renewable and sustainable energy resources to replace, even partially, fossil fuels, which are having a serious environmental impact on global warming and climate change, excessive greenhouse emissions and deforestation. For this reason, an alternative, renewable and biodegradable combustible like biodiesel is necessary. For this purpose, waste cooking oil is a potential replacement for vegetable oils in the production of biodiesel. Direct transesterification of vegetable oils was undertaken to synthesize the biodiesel. Several variables controlled the process. The alkaline catalyst that is used, typically sodium hydroxide (NaOH) or potassium hydroxide (KOH), increases the solubility and speeds up the reaction. Therefore, the methodology that this study suggests for improving the biodiesel production is based on computing techniques for prediction and optimization of these process dimensions. The method builds and selects a group of regression models that predict several properties of biodiesel samples (viscosity turbidity, density, high heating value and yield) based on various attributes of the transesterification process (dosage of catalyst, molar ratio, mixing speed, mixing time, temperature, humidity and impurities). In order to develop it, a Box-Behnken type of Design of Experiment (DoE) was designed that considered the variables that were previously mentioned. Then, using this DoE, biodiesel production features were decided by conducting lab experiments to complete a dataset with real production properties. Subsequently, using this dataset, a group of regression models—linear regression and support vector machines (using linear kernel, polynomial kernel and radial basic function kernel)—were constructed to predict the studied properties of biodiesel and to obtain a better understanding of the process. Finally, several biodiesel optimization scenarios were reached through the application of genetic algorithms to the regression models obtained with greater precision. In this way, it was possible to identify the best combinations of variables, both independent and dependent. These scenarios were based mainly on a desire to improve the biodiesel yield by obtaining a higher heating value, while decreasing the viscosity, density and turbidity. These conditions were achieved when the dosage of catalyst was approximately 1 wt %.https://www.mdpi.com/1996-1073/11/11/2995waste cooking oilbiodieselsupport vector machinessoft computing techniques linear regressiongenetic algorithms |
| spellingShingle | Marina Corral Bobadilla Roberto Fernández Martínez Rubén Lostado Lorza Fátima Somovilla Gómez Eliseo P. Vergara González Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines Energies waste cooking oil biodiesel support vector machines soft computing techniques linear regression genetic algorithms |
| title | Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines |
| title_full | Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines |
| title_fullStr | Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines |
| title_full_unstemmed | Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines |
| title_short | Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines |
| title_sort | optimizing biodiesel production from waste cooking oil using genetic algorithm based support vector machines |
| topic | waste cooking oil biodiesel support vector machines soft computing techniques linear regression genetic algorithms |
| url | https://www.mdpi.com/1996-1073/11/11/2995 |
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