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Characterization of physico-chemical properties of biodiesel components using smart data mining approaches

机译：使用智能数据挖掘方法表征生物柴油成分的理化特性

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摘要

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 (R-2 > 0.99 and AARD < 1.7%) and the SGB models yield more accurate and confident predictions.
机译：生物柴油由于易于获取和提取，舒适的运输和储存以及较低的环境污染，因此是未来最有可能替代石油燃料的燃料。生物柴油具有广泛的分子结构，包括各种具有不同热物理性质的长链脂肪酸甲酯（FAME）和脂肪酸乙酯（FAEEs）。因此，为特殊的柴油发动机选择合适的酯类似乎需要一种可靠的估算酯类性质的方法。在本研究中，研究人员正在开发一套新颖而强大的方法，直接从多个基本参数中估算出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，等熵和等温可压缩性有效变量（即温度，压力，分子量和正常熔点）。随机梯度增强（SGB）和遗传编程（GP）作为该领域的创新和强大的数学方法，已在包括2117、1048、483和310个样本的大型数据集上应用和实现，分别用于密度，声速，等熵和等温压缩。统计评估表明，新开发的模型（R-2> 0.99，AARD <1.7％）具有很高的适用性和准确性，而SGB模型得出的预测更加准确和可信。

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