Development of SVM-based machine learning model for estimating lornoxicam solubility in supercritical solvent

Zhang, Mingji; Mahdi, Wael A.

doi:10.1016/j.csite.2023.103268

Cited by 7 publications

(1 citation statement)

References 15 publications

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“…The study revealed that the SVM-quadratic model displayed an impressive R 2 of 0.967, indicating a strong correlation between the predicted and actual solubility values. In a similar study, a quadratic support vector machine was found to be robust in modelling fault diagnosis methodology for nuclear power plants as reported by [30]. The use of the quadratic kernel function has proven to be efficacious when dealing with datasets that include a large number of dimensions but have a comparatively limited quantity of training samples [31].…”

Section: Predictive Performance Of the Modelsmentioning

confidence: 72%

Numerical Analysis of Gas Hold-Up of Two-Phase Ebullated Bed Reactor

Almukhtar,

Yahya,

Mahdy

et al. 2023

ChemEngineering

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Due to the significant increase in heavy feedstocks being transported to refineries and the hydrocracking process, the significance of adopting an ebullated bed reactor has been reemphasized in recent years. The predictive modelling of gas hold-up in an ebullated two-phase reactor was performed using 10 machine learning methods based on support vector machine (SVM) and Gaussian process regression (GPR) in this study. In an ebullated bed reactor, the impacts of three features, namely liquid velocity, gas velocity, and recycling ratio, on the gas hold-up were examined. The liquid velocity has the most impact on the predicted gas hold-up, according to the feature significance analysis. The rotational-quadratic, squared-exponential, Matern 5/2, and exponential kernel functions integrated with the GPR models and the linear, quadratic, cubic, fine, medium, and coarse kernel functions integrated with the SVM model performed well during training and testing, with the exception of the fine SVM model, whose R2 is very low. According to the R2 > 0.9 and low RMSE and MAE values, the rotational-quadratic, squared-exponential, and Matern 5/2 GPR models performed the best.

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Section: Predictive Performance Of the Modelsmentioning

confidence: 72%