Nowadays, the high consumption of fossil fuels has caused many pollutants and environmental problems. Biodiesel has recently been considered as a clean and renewable alternative to fossil fuels. They are found in some molecular structures including fatty acid ethyl esters (FAEEs) and also fatty methyl esters (FAMEs), having various thermophysical characteristics. Thus, it appears essential to select the suitable methods for a particular diesel engine to estimate the ester characteristics. The current research sets out to develop a new and robust method predicting isothermal compressibility of long-chain fatty acid methyl and ethyl esters directly from several basic efficient parameters (pressure, temperature, normal melting point, and molecular weight). Therefore, as a novel and prevailing mathematical method in this field, an extreme learning machine was implemented for isothermal compressibility on the massive dataset. According to statistical evaluations, this novel established model had high accuracy and applicability (R² = 1 and RMSE = 0.0018714) which is more accurate than previous models presented by former researchers. Among various factors of the sensitivity analysis, temperature and pressure had the greatest effect on the output values, so that the output parameter has a direct relationship with temperature and an inverse relationship with pressure with relevancy factors of 22.44% and −79.81%.

中文翻译：

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开发一个基于极限学习机器的模型来估计生物柴油的等温压缩性

如今，化石燃料的高消耗造成了许多污染物和环境问题。生物柴油最近被认为是化石燃料的清洁和可再生替代品。它们存在于一些分子结构中，包括脂肪酸乙酯 (FAEE) 和脂肪甲酯 (FAME)，具有各种热物理特性。因此，为特定的柴油发动机选择合适的方法来估计酯特性似乎是必不可少的。目前的研究着手开发一种新的、可靠的方法，直接从几个基本的有效参数（压力、温度、正常熔点和分子量）预测长链脂肪酸甲酯和乙酯的等温压缩性。因此，作为该领域一种新颖而流行的数学方法，在大量数据集上实现了等温可压缩性的极限学习机。根据统计评估，这种新建立的模型具有较高的准确性和适用性（R ²  = 1 和 RMSE = 0.0018714），这比以前研究人员提出的模型更准确。在灵敏度分析的各种因素中，温度和压力对输出值的影响最大，因此输出参数与温度成正比，与压力成反比，相关系数分别为22.44%和-79.81%。