{"title":"利用响应面方法优化蟒蛇脂肪油的超声波辅助生物柴油生产","authors":"Em Canh Pham , Dat Van Nguyen","doi":"10.1016/j.nexus.2024.100331","DOIUrl":null,"url":null,"abstract":"<div><div>Diversification of oil feedstocks for biodiesel production is very necessary to reduce dependence on traditional vegetable oils and animal fats. Therefore, a conventional and ultrasound-assisted single-step transesterification process was optimized using response surface methodology (RSM) for biodiesel production from a novel feedstock python fat oil (PFO). Second-order polynomial models of the conventional (CM) and ultrasound-assisted (USM) methods were used to predict the biodiesel yield, and the coefficient of determination (R<sup>2</sup>) was found to be at 0.9946, and 0.9873, respectively. The optimal biodiesel yield of USM calculated from the model is 99.12 % with the following reaction conditions: PFO/methanol ratio of 33.77 wt%, PFO/KOH ratio of 1.05 wt%, and reaction time of 128.53 min. Biodiesel yield results under optimal conditions have demonstrated that the regression models are consistent with experimental data. Besides, the biodiesel yield of USM (98.90 %) was significantly higher than that of CM (92.73 %). In particular, the properties of PFO biodiesel produced under optimal conditions were found to agree with EN 14,214 standard specifications. In summary, single-step biodiesel production from the PFO new feedstock with USM can be commendably used to engender and adopt a more sustainable and environmentally friendly energy approach.</div></div>","PeriodicalId":93548,"journal":{"name":"Energy nexus","volume":"16 ","pages":"Article 100331"},"PeriodicalIF":8.0000,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimization of ultrasound-assisted biodiesel production from python fat oil using response surface methodology\",\"authors\":\"Em Canh Pham , Dat Van Nguyen\",\"doi\":\"10.1016/j.nexus.2024.100331\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Diversification of oil feedstocks for biodiesel production is very necessary to reduce dependence on traditional vegetable oils and animal fats. Therefore, a conventional and ultrasound-assisted single-step transesterification process was optimized using response surface methodology (RSM) for biodiesel production from a novel feedstock python fat oil (PFO). Second-order polynomial models of the conventional (CM) and ultrasound-assisted (USM) methods were used to predict the biodiesel yield, and the coefficient of determination (R<sup>2</sup>) was found to be at 0.9946, and 0.9873, respectively. The optimal biodiesel yield of USM calculated from the model is 99.12 % with the following reaction conditions: PFO/methanol ratio of 33.77 wt%, PFO/KOH ratio of 1.05 wt%, and reaction time of 128.53 min. Biodiesel yield results under optimal conditions have demonstrated that the regression models are consistent with experimental data. Besides, the biodiesel yield of USM (98.90 %) was significantly higher than that of CM (92.73 %). In particular, the properties of PFO biodiesel produced under optimal conditions were found to agree with EN 14,214 standard specifications. In summary, single-step biodiesel production from the PFO new feedstock with USM can be commendably used to engender and adopt a more sustainable and environmentally friendly energy approach.</div></div>\",\"PeriodicalId\":93548,\"journal\":{\"name\":\"Energy nexus\",\"volume\":\"16 \",\"pages\":\"Article 100331\"},\"PeriodicalIF\":8.0000,\"publicationDate\":\"2024-10-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy nexus\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772427124000627\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy nexus","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772427124000627","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Optimization of ultrasound-assisted biodiesel production from python fat oil using response surface methodology
Diversification of oil feedstocks for biodiesel production is very necessary to reduce dependence on traditional vegetable oils and animal fats. Therefore, a conventional and ultrasound-assisted single-step transesterification process was optimized using response surface methodology (RSM) for biodiesel production from a novel feedstock python fat oil (PFO). Second-order polynomial models of the conventional (CM) and ultrasound-assisted (USM) methods were used to predict the biodiesel yield, and the coefficient of determination (R2) was found to be at 0.9946, and 0.9873, respectively. The optimal biodiesel yield of USM calculated from the model is 99.12 % with the following reaction conditions: PFO/methanol ratio of 33.77 wt%, PFO/KOH ratio of 1.05 wt%, and reaction time of 128.53 min. Biodiesel yield results under optimal conditions have demonstrated that the regression models are consistent with experimental data. Besides, the biodiesel yield of USM (98.90 %) was significantly higher than that of CM (92.73 %). In particular, the properties of PFO biodiesel produced under optimal conditions were found to agree with EN 14,214 standard specifications. In summary, single-step biodiesel production from the PFO new feedstock with USM can be commendably used to engender and adopt a more sustainable and environmentally friendly energy approach.
Energy nexusEnergy (General), Ecological Modelling, Renewable Energy, Sustainability and the Environment, Water Science and Technology, Agricultural and Biological Sciences (General)