Green diesel, also known as renewable diesel, is a biofuel obtained from biomass with similar physical properties than petroleum diesel. Renewable diesel is mainly composed by hydrocarbons free of oxygen, which can be blended at high concentrations with petroleum diesel due to their chemical affinity. Density and viscosity are two important physical properties of fuels that play an important role in the processes of atomization and injection in engines. Also, the knowledge of these properties is important for the correct design, operation, and optimization of chemical processes. Despite their importance, there is low information about experimental data and developed models related to the renewable diesel–petroleum diesel blend. To overcome the above limitation, this paper is focused on the study of four blends of renewable diesel and petroleum diesel in order to study the variation of density and kinematic viscosity with respect to the concentration and temperature. In addition, empirical correlations to predict the density and kinematic viscosity as function of temperature and concentration were developed. Density and viscosity of renewable diesel, petroleum diesel, and their blends were simultaneously measured with a densitometer–viscometer device with an uncertainty in density and viscosity of 0.1 kg/m³ and 1 × 10⁻⁸ m²/s. The parameters of the developed correlations were adjusted by the Levenberg–Marquardt optimization method. There was a good agreement between the calculated and experimental data because an average absolute deviation (AAD) of 0.07 and 1.66% were obtained for the predictions of density and kinematic viscosity respectively.

绿色柴油，也称为可再生柴油，是一种从生物质中获得的生物燃料，其生物特性与石油柴油相似。可再生柴油主要由不含氧的碳氢化合物组成，由于它们的化学亲和力，它们可以高浓度与石油柴油混合。密度和粘度是燃料的两个重要物理特性，它们在发动机的雾化和喷射过程中起着重要的作用。同样，这些特性的知识对于化学过程的正确设计，操作和优化也很重要。尽管它们很重要，但有关可再生柴油-石油-柴油混合燃料的实验数据和开发模型的信息很少。为了克服上述限制，为了研究密度和运动粘度随浓度和温度的变化，本文着重研究可再生柴油和石油柴油的四种混合物。此外，还建立了经验相关性来预测密度和运动粘度随温度和浓度的变化。使用密度计-粘度计设备同时测量可再生柴油，石油柴油及其混合物的密度和粘度，密度和粘度的不确定度为0.1 kg / m³和1×10 ^-8  m ² / s。通过Levenberg-Marquardt优化方法调整了已建立的相关性的参数。在计算数据和实验数据之间有一个很好的一致性，因为对于密度和运动粘度的预测分别获得了0.07和1.66％的平均绝对偏差（AAD）。