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<p>Polyethylene glycol (PEG) is extensively utilized and its persistence in wastewater, soils, and aquatic environments has raised concerns regarding environmental contamination and ecological impacts. Understanding and predicting PEG’s heat capacity is therefore not only critical for material design but also for assessing its stability, transformation, and degradation under environmental conditions. This study develops advanced machine learning models including Gradient Boosting Decision Tree (GBDT) combined with Evolutionary Strategies (ES), Bayesian Probability Improvement (BPI), Batch Bayesian Optimization (BBO), and Gaussian Process Optimization (GPO) to forecast PEG’s molar heat capacity, using a databank of 528 experimental observations. Analysis of correlations demonstrated that temperature dominates the predictive framework (score 0.69), while molar mass provides a secondary but meaningful effect (score 0.51). Monte Carlo-based outlier detection validated data reliability, while sensitivity and SHapley Additive exPlanations (SHAP) analyses reinforced the primacy of temperature in determining heat capacity. The comparative analysis demonstrated that GBDT-ES provided the most accurate predictions, achieving an R<sup>2</sup> of 0.998 and an average absolute relative error of 12.825%. Accurate modeling of PEG’s heat capacity provides useful insight into its thermal behavior, which may support broader assessments of its stability under environmental conditions. While the model does not directly evaluate environmental degradation, the predicted thermal trends can complement existing studies on PEG’s persistence and transformation.</p>

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Data driven based investigation of polyethylene glycol heat capacity

  • 期刊论文

  • 发表日期：

    2026年5月13日

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  • Jamal I. Al-Nabulsi,
  • Zaid Ajzan Alsalami,
  • J. Deepak,
  • M. G. M. Johar,
  • Anupama Routray,
  • A. Karthikeyan,
  • Harjot Singh Gill,
  • Yashwant Singh Bisht,
  • Ahmad Abumalek

摘要

Polyethylene glycol (PEG) is extensively utilized and its persistence in wastewater, soils, and aquatic environments has raised concerns regarding environmental contamination and ecological impacts. Understanding and predicting PEG’s heat capacity is therefore not only critical for material design but also for assessing its stability, transformation, and degradation under environmental conditions. This study develops advanced machine learning models including Gradient Boosting Decision Tree (GBDT) combined with Evolutionary Strategies (ES), Bayesian Probability Improvement (BPI), Batch Bayesian Optimization (BBO), and Gaussian Process Optimization (GPO) to forecast PEG’s molar heat capacity, using a databank of 528 experimental observations. Analysis of correlations demonstrated that temperature dominates the predictive framework (score 0.69), while molar mass provides a secondary but meaningful effect (score 0.51). Monte Carlo-based outlier detection validated data reliability, while sensitivity and SHapley Additive exPlanations (SHAP) analyses reinforced the primacy of temperature in determining heat capacity. The comparative analysis demonstrated that GBDT-ES provided the most accurate predictions, achieving an R2 of 0.998 and an average absolute relative error of 12.825%. Accurate modeling of PEG’s heat capacity provides useful insight into its thermal behavior, which may support broader assessments of its stability under environmental conditions. While the model does not directly evaluate environmental degradation, the predicted thermal trends can complement existing studies on PEG’s persistence and transformation.