Nov 2026· Chemical Engineering Communications· Vol 213, pp. 2061 - 2071· 0 citations· 43 references
TL;DR
The present study explores the application of machine learning techniques to predict the E% for caproic acid systems using available variables prior to experimentation, and the XGBoost model shows the best performance.
The results demonstrated that the Gradient Boosting algorithm can achieve the best performance in predicting chlorophenol removal, followed by the XGBoost algorithm, but neural networks performed the worst.
E. M. Hameed, Ahmed Abdul Azeez Ismael, Ali Mahmood Khalaf· Advances in Science and Tech...· 0 citations
The present study demonstrates the integration of the linear solvation energy relationship (LSER) concept with machine learning (ML) methodologies to improve the predictive and interpretative capabilities of chromatographic retention modeling. Immobilized artificial membrane (IAM) chromatography was employed as a mod...
W. Nisterenko, K. Greber, Magdalena Kierkowicz et al.· Molecular Pharmaceutics· 0 citations
This work aimed to address the challenge of developing holistic theoretical framework that can predict the corrosion inhibition efficiency and evaluate the anti-corrosion potentials of novel materials by integrating machine learning-based Quantitative Structure–Property Relationship (QSPR) models with computational sim...
C. I. Ekeocha, Anthony C. Ozurumba, I. Uzochukwu et al.· Journal of Computer-Aided Mo...· 0 citations
Six machine learning algorithms were employed to construct artificial intelligence models for the precise prediction of self-compacting concrete (SCC) flow properties, and the extreme gradient boosting (XGB) model was identified as exhibiting superior predictive accuracy and generalization performance.
Jinlei Mu, Xin Fang, Ke-qiang Cao et al.· Materials Research Express· 0 citations
Machine learning (ML) models for alkali-activated concrete (AAC) are almost universally evaluated with random train–test splits, yet the literature-compiled datasets are strongly clustered by source study, and the reliability of such evaluations has rarely been quantified. The novelty of this study is a systematic quan...
F. Pacheco-Torgal, Saqib Iqbal· Construction Materials· 0 citations
Accurate prediction of cellulose concentration in lyocell slurry is crucial for process control and product quality in sustainable lyocell fiber production, yet the complex, nonlinear nature of the swelling process makes it challenging to model using conventional parametric methods. This study develops a hybrid ensembl...
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