Bahrani Esmaeil, Shafeeyan Mohammad Saleh, Banihashemi Morteza
Department of Chemical Engineering, Faculty of Engineering, Golestan University, Aliabad Katoul, Iran.
Department of Chemical Engineering, Babol University of Technology, Babol, Iran.
Heliyon. 2024 Aug 16;10(17):e36453. doi: 10.1016/j.heliyon.2024.e36453. eCollection 2024 Sep 15.
This article presents a novel approach to predict the flash temperature of biodiesel and ethanol mixtures using the Group Contribution Method (GCM). Expanding on the pioneering work by Liaw et al. (2003), our method employs GCM to calculate the activity coefficients of biodiesel and ethanol components in the mixture. Estimating these coefficients, crucial for accurate flash temperature prediction, involves a comprehensive analysis of composition, functional groups, and vapor-liquid equilibrium (VLE) data. For this purpose, the composition of the mixture components in biodiesel, the functional groups within each biodiesel component, the composition ratios of biodiesel and ethanol in the mixture, and the functional groups present in ethanol are considered. Given that the use of UNIQUAC and NRTL models requires estimating adjustable parameters, VLE data for ethanol and biodiesel mixtures are employed to calculate the activity coefficients. This approach not only aids in estimating these coefficients but also facilitates determining the values associated with each functional group. Flash temperature predictions for biodiesel and ethanol mixtures obtained through various models, including the ideal solution, UNIQUAC, NRTL, and our proposed GCM, are rigorously assessed. The results indicate that the GCM method outperforms the alternatives, exhibiting the lowest error with a deviation of just 1.72 K compared to deviations of 1.77 K, 1.75 K, and 1.73 K for the ideal solution, UNIQUAC, and NRTL models, respectively. This research offers a promising approach for flash point estimation in complex systems, such as biodiesel-ethanol blends, contributing to the ongoing exploration in this field.
本文提出了一种使用基团贡献法（GCM）预测生物柴油和乙醇混合物闪点温度的新方法。在Liaw等人（2003年）的开创性工作基础上，我们的方法采用GCM来计算混合物中生物柴油和乙醇组分的活度系数。估计这些系数对于准确预测闪点温度至关重要，这涉及对组成、官能团和汽液平衡（VLE）数据的全面分析。为此，考虑了生物柴油中混合组分的组成、每个生物柴油组分中的官能团、混合物中生物柴油和乙醇的组成比例以及乙醇中存在的官能团。鉴于使用UNIQUAC和NRTL模型需要估计可调参数，因此采用乙醇和生物柴油混合物的VLE数据来计算活度系数。这种方法不仅有助于估计这些系数，还便于确定与每个官能团相关的值。通过各种模型（包括理想溶液、UNIQUAC、NRTL和我们提出的GCM）对生物柴油和乙醇混合物的闪点温度预测进行了严格评估。结果表明，GCM方法优于其他方法，与理想溶液、UNIQUAC和NRTL模型的偏差分别为1.77 K、1.75 K和1.73 K相比，其偏差仅为1.72 K，误差最低。这项研究为复杂系统（如生物柴油 - 乙醇混合物）的闪点估计提供了一种有前景的方法，有助于该领域的持续探索。
