This work aimed to demonstrate that a simple modification to the previously developed rough hard-sphere-chain (RHSC) model would significantly improve the accuracy of that model for viscosities of fatty acid esters and biodiesel fuels at extended pressures up to 200 MPa and higher isotherms. The new finding of this work is the temperature dependence of the exponential factor of the roughness factor (RF) of the earlier RHSC model as the accuracy of the original model (with an average absolute relative deviation, AARD of 8.29 % for 715 data points examined) was significantly improved achieving the AARD of 3.77 % once a universal function of reduced temperature replaced the original exponential factor of 6.4 × 10−4 for RF. Besides, the predictive capability of the modified RHSC model has been compared with original RHSC model and several previously developed semi-empirical models based on friction theory and free volume theory in literature. Expanding AARD on the progress in deep learning, our research introduces Artificial Neural Network (ANN) model that is simpler than previous models while maintaining high viscosity correlation accuracy for fatty acid esters and biodiesel fuels. The refined ANN model, with a single hidden layer and sigmoid activation function, achieved an AARD% of 0.78 %. Additionally, we conducted a thorough comparison with other deep learning architectures, affirming the effectiveness of our simplified approach for viscosity correlations.

中文翻译：

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基于改进的粗糙硬球链模型和深度学习方法的脂肪酸酯和生物柴油燃料高压黏度建模


这项工作旨在证明，对先前开发的粗糙硬球链 （RHSC） 模型进行简单修改将显著提高该模型在高达 200 MPa 的扩展压力和更高等温线下脂肪酸酯和生物柴油燃料粘度的准确性。这项工作的新发现是早期 RHSC 模型的粗糙度因子 （RF） 的指数因子的温度依赖性，因为原始模型的准确性（平均绝对相对偏差，检查的 715 个数据点的 AARD 为 8.29%）得到显着提高，达到 3.77% 的 AARD，一旦降低温度的通用函数取代了 RF 的原始指数因子 6.4 × 10−4。此外，已将改进的 RHSC 模型的预测能力与原始 RHSC 模型以及文献中基于摩擦理论和自由体积理论的几个先前开发的半经验模型进行了比较。我们的研究在深度学习的进展上扩展了 AARD，引入了人工神经网络 （ANN） 模型，该模型比以前的模型更简单，同时保持了脂肪酸酯和生物柴油燃料的高粘度相关精度。改进的 ANN 模型具有单个隐藏层和 S 形激活函数，实现了 0.78% 的 AARD%。此外，我们与其他深度学习架构进行了全面比较，肯定了我们简化的粘度相关性方法的有效性。