Abstract Biodiesels are the most probable future alternatives for petroleum fuels due to their easy accessibility and extraction, comfortable transportation and storage and lower environmental pollutions. Biodiesels have wide range of molecular structures including various long chain fatty acid methyl esters (FAMEs) and fatty acid ethyl esters (FAEEs) with different thermos-physical properties. Therefore, reliable methods estimating the ester properties seems necessary to choose the appropriate one for a special diesel engine. In the present study, the effort was developing a set of novel and robust methods for estimation of four important properties of common long chain fatty acid methyl and ethyl esters including density, speed of sound, isentropic and isothermal compressibility, directly from a number of basic effective variables (i.e. temperature, pressure, molecular weight and normal melting point). Stochastic gradient boosting (SGB) and genetic programming (GP) as innovative and powerful mathematical approaches in this area were applied and implemented on large datasets including 2117, 1048, 483 and 310 samples for density, speed of sound, isentropic and isothermal compressibility, respectively. Statistical assessments revealed high applicability and accuracy of the new developed models (R2 > 0.99 and AARD

中文翻译：

---

使用智能数据挖掘方法表征生物柴油组分的理化特性

摘要 生物柴油由于其易于获取和提取、运输和储存舒适以及对环境污染低等优点成为未来石油燃料最有可能的替代品。生物柴油具有广泛的分子结构，包括具有不同热物理性质的各种长链脂肪酸甲酯（FAME）和脂肪酸乙酯（FAEE）。因此，为特殊柴油发动机选择合适的酯类性能的可靠方法似乎是必要的。在本研究中，我们的工作是开发一套新颖且稳健的方法，用于直接从许多基本的基础数据中估算常见的长链脂肪酸甲酯和乙酯的四个重要特性，包括密度、声速、等熵和等温压缩性。有效变量（即 温度、压力、分子量和正常熔点）。随机梯度提升 (SGB) 和遗传规划 (GP) 作为该领域创新和强大的数学方法被应用和实施在大型数据集上，包括 2117、1048、483 和 310 个样本，分别用于密度、声速、等熵和等温可压缩性. 统计评估显示新开发的模型具有很高的适用性和准确性（R2 > 0.99 和 AARD