TY  - JOUR
EP  - S212
PB  - John Wiley and Sons Inc
SN  - 10668527
N1  - cited By 0
TI  - Prediction of ionic liquids toxicity using machine learning models for application to gas hydrate
SP  - S199
AV  - none
A1  - Abdullah, N.H.
A1  - Zaini, D.
A1  - Lal, B.
JF  - Process Safety Progress
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189079520&doi=10.1002%2fprs.12599&partnerID=40&md5=c30a79a02cfb7b1d15b52063a7d804bd
VL  - 43
Y1  - 2024///
N2  - Ionic liquids (ILs) are highly favored in the oil and gas industry as gas hydrate inhibitors due to their dual functionality as thermodynamic inhibitor and kinetic hydrate inhibitor. Though known as the â??green alternatives,â?? concerns about the effects of ILs in the environment are rising such that ILs can stabilize in water systems. Furthermore, there are insufficient data on the toxicity of ILs, limiting the use of ILs for industrial applications. Ridge, LASSO, decision tree, random forest, extra tree, gradient boost, and support vector regressions were used to develop IL toxicity predictive models. Random forest yielded the strongest predictive performance, scoring the highest R2 value of 0.86, with mean absolute error and root mean square error values of 0.32 and 0.43, respectively. Feature selections were conducted to investigate the contributions of the five molecular descriptors involved in developing regression models in this work. Descriptor MSDC was found to contribute the highest at 67 in predicting the toxicity of ILs, followed by SNarA and MAXDPC, demonstrating contributions of 15.2 and 14.1, respectively. Further quantitative structureâ??activity relationship model validations were executed; the use of three descriptors resulted in a 2 increase in predictive performance for decision tree regression, whereas R2 values remained the same for random forest, extra tree, and gradient boosting. © 2024 American Institute of Chemical Engineers.
IS  - S1
ID  - scholars19699
KW  - Gas hydrates; Gas industry; Hydration; Ionic liquids; Learning systems; Machine learning; Mean square error
KW  -  Descriptors; Extra-trees; Gradient boost; LASSO; Machine learning models; Oil and Gas Industry; Predictive performance; Random forests; Ridge; Support vector regressions
KW  -  Decision trees
ER  -