C_(6)~C_(10)烷醇的SAFT-VR Mie状态方程参数回归及其热物性研究

Estimation of SAFT-VR Mie equation of state parameters and thermodynamic properties ofC_(6)~C_(10)alcohols

统计缔合流体理论(SAFT)状态方程对长链烷醇的热物性研究具有重要意义,而状态方程参数的获取是流体热物性研究的基础。基于SAFT-VRMie状态方程,采用Levenberg-Marquardt算法并结合相平衡、过冷液相密度和声速性质的参数回归策略,获取C_(6)~C_(10)烷醇的状态方程模型参数。进一步评估SAFT-VRMie状态方程对C_(6)~C_(10)烷醇在宽温度和压力范围内的相平衡和热力学偏导特性的预测性能,并与PC-SAFT状态方程进行比较。结果表明,SAFT-VRMie对五种烷醇整体具有更优的饱和蒸气压、饱和液相密度、蒸发焓、过冷液相密度、比定压热容和声速预测性能,平均预测偏差分别为0.74%、0.82%、3.02%、0.54%、2.88%和2.31%。同时,SAFTVR Mie具有可靠的外推预测能力,其对高压声速的预测结果与实验数据具有较好的一致性。此外,SAFT-VR Mie对压力-密度导数的不合理描述是造成声速预测偏差的主要原因。改进分子间单体和缔合相互作用能够有效提高比热容的预测精度,为长链烷醇缔合流体的热物性预测提供更好的理论关联模型。

The statistical associating fluid theory(SAFT)equation of state(EoS)is of great significance for the thermodynamic properties study of long-chain alcohols.The acquisition of SAFT-type EoS parameters is the basis for the thermodynamic properties study of fluids.The SAFT-VR Mie EoS parameters for C_(6)~C_(10)alcohols were obtained by using a parametric regression strategy based on the Levenberg-Marquardt algorithm and combining phase equilibrium,condensed-liquid density,and speed of sound properties.The predictive performance of SAFTVR Mie EoS based on the new parameters for phase equilibrium and thermodynamic derivative properties over a wide temperature and pressure range was further evaluated.The results show that SAFT-VR Mie EoS exhibits excellent prediction performance of saturated vapor pressure,saturated liquid phase density,enthalpy of vaporization,condensed-liquid density,isobaric specific heat capacity,and speed of sound for C_(6)~C_(10)alcohols with average prediction deviations of 0.74%,0.82%,3.02%,0.54%,2.88%,and 2.31%,respectively.Meanwhile,SAFTVR Mie EoS provides reliable extrapolation prediction capability,and its prediction results of speed of sound for high pressure show satisfactory agreement with experimental data.In addition,the unreasonable description of the pressure-density derivative by SAFT-VR Mie EoS is the primary reason for the deviations of speed of sound predictions.Improving the intermolecular monomer and association interactions can effectively improve the prediction accuracy of specific heat capacity,and provide a better theoretical correlation model for the prediction of thermodynamic properties of long-chain alcohols associating fluids.