Investigation of the effectiveness of edible oils as solvent in reactive extraction of some hydroxycarboxylic acids and modeling with multiple artificial intelligence models
Yükleniyor...
Dosyalar
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/embargoedAccess
Özet
This 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.
Açıklama
Anahtar Kelimeler
Carboxylic acid, Reactive extraction, Edible oil, Chemical experiment prediction model, Machine learning
Kaynak
Biomass Conversion and Biorefinery
WoS Q Değeri
Q2
Scopus Q Değeri
N/A
Cilt
13
Sayı
14
