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<p>Fatty acid methyl esters (FAMEs) are extensively utilized as pure biofuels due to their renewable origin, biodegradability, and favorable environmental performance. In this study, a wide range of machine learning techniques including Decision Trees, Lasso Regression, AdaBoost, Ensemble Learning, k-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), Multilayer Perceptron Artificial Neural Networks (MLP-ANN), Random Forest, and Support Vector Regression (SVR) were employed to develop accurate predictive models for FAME density. The models were trained using a comprehensive dataset comprising 2924 experimental density measurements collected from the literature, covering wide ranges of temperature, pressure, molecular weight, and elemental composition. Model performance was rigorously evaluated using statistical metrics such as the coefficient of determination (R²), mean squared error (MSE), and absolute average relative error (AARE%). Among the investigated approaches, the Ensemble Learning model achieved the highest predictive accuracy, yielding an R² of 0.982, a low MSE of 27.75, and an AARE% below 0.14%. Sensitivity analysis and SHAP (SHapley Additive exPlanations) evaluation consistently identified temperature as the dominant factor influencing FAME density, followed by pressure, while molecular weight and elemental composition exhibited comparatively weaker effects. The reliability of the dataset was further confirmed through leverage-based outlier detection. Overall, the proposed data-driven framework provides a cost-effective and reliable alternative to experimental density measurements, enabling accurate density estimation of FAME-based biofuels over broad operating conditions.</p>

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Reliable Model Development of Density for Pure Biofuels via Data Driven Approaches

  • 期刊论文

  • 发表日期：

    2026年3月4日

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  • Mahran Al-Zyoud,
  • Magdi E. A. Zaki,
  • Raed Alfilh,
  • Gadug Sudhamsu,
  • Prabhat Kumar Sahu,
  • Murari Devakannan Kamalesh,
  • Sumit Sharma,
  • Sobhi M. Gomha,
  • Nargis Yousafzai

摘要

Fatty acid methyl esters (FAMEs) are extensively utilized as pure biofuels due to their renewable origin, biodegradability, and favorable environmental performance. In this study, a wide range of machine learning techniques including Decision Trees, Lasso Regression, AdaBoost, Ensemble Learning, k-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), Multilayer Perceptron Artificial Neural Networks (MLP-ANN), Random Forest, and Support Vector Regression (SVR) were employed to develop accurate predictive models for FAME density. The models were trained using a comprehensive dataset comprising 2924 experimental density measurements collected from the literature, covering wide ranges of temperature, pressure, molecular weight, and elemental composition. Model performance was rigorously evaluated using statistical metrics such as the coefficient of determination (R²), mean squared error (MSE), and absolute average relative error (AARE%). Among the investigated approaches, the Ensemble Learning model achieved the highest predictive accuracy, yielding an R² of 0.982, a low MSE of 27.75, and an AARE% below 0.14%. Sensitivity analysis and SHAP (SHapley Additive exPlanations) evaluation consistently identified temperature as the dominant factor influencing FAME density, followed by pressure, while molecular weight and elemental composition exhibited comparatively weaker effects. The reliability of the dataset was further confirmed through leverage-based outlier detection. Overall, the proposed data-driven framework provides a cost-effective and reliable alternative to experimental density measurements, enabling accurate density estimation of FAME-based biofuels over broad operating conditions.