Biodiesel is assumed a renewable and environmentally friendly fuel that possesses the potential to substitute petroleum diesel. The basic purpose of the present study is to design a precise algorithm based on Gaussian Process Regression (GPR) model with several kernel functions, i.e., Rational Quadratic, Squared Exponential, Matern, and Exponential, to estimate biodiesel properties. These properties include kinematic viscosity (KV), pour point (PP), iodine value (IV), and cloud point (CP) as a function of fatty acid composition. In order to develop this model, some variables are assumed, such as molecular weight, carbon number, double bond numbers, monounsaturated fatty acids, polyunsaturated fatty acid, weight percent of saturated acid, and temperature. The performance and efficiency of the GPR model are measured through several statistical criteria and the results are summarized in root mean square error (RMSE) and coefficients of determination (). and RMSE are sorted as 0.992 & 0.15697, 0.998 & 0.96580, 0.966 & 1.38659, and 0.968 & 1.56068 for four properties such as KV, IV, CP, and PP, respectively. It is worth to mention this point that the kernel function Squared Exponential shows a great performance for IV and PP and kernel functions Exponential and Matern indicate appropriate efficiency for CP and KV properties, respectively. On the other hand, the results of the offered GPR models are compared with those of the previous models, LSSVM-PSO and ANFIS. The outcomes proved the superiority of this model over two former models in point of estimating the biodiesel properties.

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开发基于高斯过程回归方法的稳健模型来预测生物柴油特性

生物柴油被认为是一种可再生且环保的燃料，具有替代石油柴油的潜力。本研究的基本目的是设计一种基于高斯过程回归 (GPR) 模型的精确算法，该模型具有多个核函数，即有理二次、平方指数、Matern 和指数，以估计生物柴油的特性。这些特性包括作为脂肪酸组成函数的运动粘度 (KV)、倾点 (PP)、碘值 (IV) 和浊点 (CP)。为了开发该模型，假设了一些变量，例如分子量、碳数、双键数、单不饱和脂肪酸、多不饱和脂肪酸、饱和酸的重量百分比和温度。）。 对于 KV、IV、CP 和 PP 四个属性，RMSE 和 RMSE 分别排序为 0.992 & 0.15697、0.998 & 0.96580、0.966 & 1.38659 和 0.968 & 1.56068。值得一提的是，核函数 Squared Exponential 对 IV 和 PP 表现出很好的性能，核函数 Exponential 和 Matern 分别表明了对 CP 和 KV 属性的适当效率。另一方面，将提供的 GPR 模型的结果与以前的模型、LSSVM-PSO 和 ANFIS 的结果进行比较。结果证明了该模型在估计生物柴油特性方面优于两个以前的模型。