Abdelfattah Walid, Abosaoda Munthar Kadhim, Doshi Hardik, Shreenidhi H S, Parhi Manoranjan, Singh Devendra, Singh Prabhjot, Kandahari Abdolali Yarahmadi
Department of Mathematics, College of Science, Northern Border University, Arar, Saudi Arabia.
College of pharmacy, the Islamic University, Najaf, Iraq.
Sci Rep. 2025 Aug 22;15(1):30961. doi: 10.1038/s41598-025-16547-8.
Fatty acid ethyl esters (FAEEs) are widely used in biofuels, pharmaceuticals, and lubricants, offering an eco-friendly alternative due to their biodegradability and renewable nature, contributing to environmental sustainability. The objective of this study is to construct advanced predictive algorithms using various machine learning methods, including AdaBoost, Decision Trees, KNN, Random Forests, Ensemble Learning, CNN, and SVR. These models aim to accurately predict the density of FAEEs across different temperature, pressure, molar mass, and elemental composition (oxygen, carbon, and hydrogen content). Experimental data reported in earlier publications were employed to develop the models. Results indicate that the dataset is highly well-suited for developing reliable models based on data. Analysis reveals that temperature exerts a considerable influence on density, with pressure also playing a critical role. The reliability of the dataset, consisting of 1307 experimental datapoints gathered from the literature, was ensured through the application of a Monte Carlo outlier detection algorithm, which validated its suitability for model training and validation. Through extensive statistical evaluations and visualization techniques, SVR emerged as the most accurate model for density prediction. Sensitivity analysis confirms the influence of all input features, with SHAP analysis identifying temperature as the most dominant factor affecting density. The developed framework provides an economical and time-saving substitute for laboratory-based experimentation density measurements, enabling precise density estimation for FAEEs under various conditions.
脂肪酸乙酯（FAEEs）广泛应用于生物燃料、制药和润滑剂领域，由于其生物可降解性和可再生性，提供了一种环保替代品，有助于实现环境可持续性。本研究的目的是使用各种机器学习方法构建先进的预测算法，包括AdaBoost、决策树、KNN、随机森林、集成学习、卷积神经网络（CNN）和支持向量回归（SVR）。这些模型旨在准确预测FAEEs在不同温度、压力、摩尔质量和元素组成（氧、碳和氢含量）下的密度。利用早期出版物中报道的实验数据来开发这些模型。结果表明，该数据集非常适合基于数据开发可靠的模型。分析表明，温度对密度有相当大的影响，压力也起着关键作用。通过应用蒙特卡罗异常值检测算法，确保了由从文献中收集的1307个实验数据点组成的数据集的可靠性，该算法验证了其适用于模型训练和验证。通过广泛的统计评估和可视化技术，SVR成为密度预测最准确的模型。敏感性分析证实了所有输入特征的影响，SHAP分析确定温度是影响密度的最主要因素。所开发的框架为基于实验室的实验密度测量提供了一种经济且省时的替代方法，能够在各种条件下对FAEEs进行精确的密度估计。
