Investigation of the effectiveness of edible oils as solvent in reactive extraction of some hydroxycarboxylic acids and modeling with multiple artificial intelligence models

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dc.authorid0000-0003-1517-6810en_US
dc.contributor.authorSevindik, Yunus Emre
dc.contributor.authorGök, Aslı
dc.contributor.authorLalikoglu, Melisa
dc.contributor.authorGülgün, Sueda
dc.contributor.authorGüven, Ebu Yusuf
dc.contributor.authorGürkaş-Aydın, Zeynep
dc.contributor.authorYağcı, Mehmet Yavuz
dc.contributor.authorTurna, Özgür Can
dc.contributor.authorAydın, Muhammed Ali
dc.contributor.authorAşçı, Yavuz Selim
dc.date.accessioned2023-10-27T12:07:16Z
dc.date.available2023-10-27T12:07:16Z
dc.date.issued2023en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThis study investigated the usability of diferent vegetable oils as solvents for separating citric, malic, and glycolic acids from aqueous solutions by reactive extraction method. A machine learning model was developed to predict intermediate values from the dataset created using the experimental results using multiple linear regression (MLR) and extreme gradient boosting (XGB). We used sunfower oil, corn oil, linseed oil, sweet almond oil, sesame oil, and castor oil in six types of vegetable oil. Trioctylamine (TOA) was used as an extractant in reactive extraction studies. The results obtained showed that approximately 99% of acids can be separated from their aqueous solutions when suitable mixtures of organic phases are used. Based on the results, we discovered that the XGB method outperforms the MLR method for each dataset. Thanks to the high-performance prediction model developed, it was possible to reach higher separation efciencies by determining the optimum experimental conditions. In addition, the costs and wastes associated with experiments decreased due to the developed high-performance estimation model. The reactive extraction estimation model was publicly available on GitHub and open to other researchers.en_US
dc.identifier.doi10.1007/s13399-023-03853-2en_US
dc.identifier.endpage13265en_US
dc.identifier.issue14en_US
dc.identifier.scopus2-s2.0-85147365060en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage13253en_US
dc.identifier.urihttps://hdl.handle.net/11467/6837
dc.identifier.urihttps://doi.org/10.1007/s13399-023-03853-2
dc.identifier.volume13en_US
dc.identifier.wosWOS:000926057100004en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofBiomass Conversion and Biorefineryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectCarboxylic acid, Reactive extraction, Edible oil, Chemical experiment prediction model, Machine learningen_US
dc.titleInvestigation of the effectiveness of edible oils as solvent in reactive extraction of some hydroxycarboxylic acids and modeling with multiple artificial intelligence modelsen_US
dc.typeArticleen_US

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