Flash points measurements and prediction for binary miscible mixtures

Flash point is one of the most important parameters used to characterize the potential fire and explosion hazards for flammable liquids. In this study, flash points of twenty eight binary miscible mixtures comprised eighteen flammable pure components with different compositions were measured by using the closed cup apparatus. The obtained experimental data are further employed to develop simple and accurate models for predicting the flash points of binary miscible mixtures. Based on the vapor–liquid equilibrium theory, the normal boiling point, the standard enthalpy of vaporization, the average number of carbon atoms, and the stoichiometric concentration of the gas phase were selected as the dominant physicochemical parameters that were relevant to the overall flash point property of liquids. With these parameters for pure components as well as the compositions of mixtures, the new form of characteristic physicochemical parameters for mixtures were developed and used as the input parameters for the flash point prediction of mixtures. Both the modeling methods of multiple linear regression (MLR) and multiple nonlinear regression (MNR) were employed to model the possible quantitative relationships between the parameters for mixtures and the flash points of binary miscible mixtures. The resulted models showed satisfactory prediction ability, with the average absolute error for the external test set being 2.506 K for the MLR model and 2.537 K for the MNR model, respectively, both of which were within the range of the experimental error of FP measurements. Model validation was also performed to check the stability and predictivity of the presented models, and the results showed that both models were valid and predictive. The models were further compared to other previously published models. The results indicated the superiority of the presented models and revealed which can be effectively used to predict the FP of binary miscible mixtures, requiring only some common physicochemical parameters for the pure components other than any experimental flash point or flammability limit data as well as the use of the Le Chatelier law. This study can provide a simple, yet accurate way for engineering to predict the flash points of binary miscible mixtures as applied in the assessment of fire and explosion hazards and the development of inherently safer designs for chemical processes.     

A new method for the prediction of flash points for ternary miscible mixtures

The flash point is one of the most important physicochemical parameters used to characterize the fire and explosion hazard for flammable liquids. The flash points of ternary miscible mixtures with different components and compositions were measured in this study. Four model input parameters, being normal boiling point, the standard enthalpy of vaporization, the average number of carbon atoms and the stoichiometric concentration of the gas phase for mixtures, were employed and calculated based on the theory of vapor–liquid equilibrium. Both multiple linear regression (MLR) and multiple nonlinear regression (MNR) methods were applied to develop prediction models for the flash points of ternary miscible mixtures. The developed predictive models were validated using data measured experimentally as well as taking data on flash points of ternairy mixtures from the literature. Results showed that the obtained average absolute error of both the MLR and the MNR model for all the datasets were within the range of experimental error of flash point measurements. It is shown that the presented models can be effectively used to predict the flash points of ternary mixtures with only some common physicochemical parameters.     

基于QSPR方法的脂肪醇化合物闪点预测

为提高脂肪醇化合物闪点预测精度,提出基于定量结构-性质关系(QSPR)原理的脂肪醇化合物闪点预测方法。应用Dragon软件计算出91种脂肪醇的分子描述符,利用遗传函数算法(GFA)从1 481个描述符中筛选出3个与脂肪醇闪点关系最密切的分子描述符。分别用多元线性回归(MLR)方法和支持向量机(SVM)方法进行建模,并采用内部验证和外部检验的方式对模型的拟合度、预测性等性能进行验证。结果表明:预测集的MLR方法和SVM方法的平均绝对误差(AAE)分别为2.870 K和2.706 K;均方根误差(RMSE)为3.451 K和3.371 K。SVM模型在精度上略优于MLR模型,而MLR模型更为简单和方便。     

烃类及其衍生物闪点、沸点的定量构效关系

基于定量结构一性质相关性（QSPR）原理,研究了烃类及其衍生物闪点、沸点与其分子结构间的内在定量关系。应用CODESSA软件计算384种烃类及其衍生物的分子结构描述符,建立了闪点和沸点的QSPR模型。用最佳多元线性回归（B．MLR）方法筛选得到的分子描述符建立了线性回归模型。用B-MLR方法所选择的5个描述符作为支持向量机（SVM）的输入建立了非线性模型。所有的化合物被分为训练集和测试集,对每个模型的训练集和测试集的复相关系数、交互验证系数、均方根误差等进行了计算,并用测试集对模型的预测能力进行检验,预测结果表明：预测值与实验值均符合良好,所建立的闪点模型稳健,泛化能力强,预测误差小,预测的效果令人满意,但沸点的模型预测效果有待加强。相比烃类物质的模型,加了衍生物的模型性能均有所下降。     

QSPR在混合物典型燃爆特性预测中的研究进展

为全面了解定量结构-性质关系（QSPR）方法在混合物燃爆特性预测中的研究现状,展望其发展趋势,综述其在混合物闪点、爆炸极限与自燃温度预测中的国内外研究进展,分析预测目标参数的选择、数据收集、描述符计算和筛选以及模型建立和验证等方面的不足与研究方向。结果表明:QSPR在混合物燃爆特性预测中尚处于起步阶段,当前研究的首要限制是混合物燃爆特性参数实验数据样本不足,关键点及难点是混合物结构的准确表征,未来研究应关注的重点是大量数据源统一的数据样本的获取方法、非加和性混合物分子描述符的计算方法以及机器学习等非线性建模方法。     

Flash point of biodiesel/glycerol/alcohol mixtures for safe processing and storage

Mixtures of biodiesel, glycerol, and ethanol/methanol are commonly processed and stored in biodiesel production. In this work, non-ideal models are used to calculate the Flash Points (FPs) of binary and ternary mixtures, using data available from different feedstocks. Despite the fact that biodiesel is considered safer than common diesel fuels, results show a synergistic effect of biodiesel/methanol and biodiesel/ethanol mixtures, resulting in a reduction of the flash point of mixtures to values lower than the ones of pure compounds. Most soluble ternary mixtures were found flammable, the only exception being mixtures with a relatively lower alcohol content (45% mol. ethanol or 42% methanol) at temperature lower than 303 K. Accidental increase in temperature can cause domino effect, due to the higher solubility and the formation of new flammable ternary mixtures.     

混合液体火灾爆炸危险性——闪点预测与实验研究

支持向量机应用核函数技术,已经成为当前国际上一个研究的热点,由于支持向量机具有良好的理论基础和泛化性能,可将其引入到混合液体闪点预测的研究之中,以期建立准确、高效的预测模型。本文建立了一个基于支持向量机的理论模型,用于预测二元互溶混合液体的闪点。根据所研究混合液体的物理性质,选择了纯物质的粘度、表面张力、配比、燃烧下限等物理参数来表征闪点,以这些参数作为输入参数,二元混合液体的闪点作为输出值,应用支持向量机方法对两者之间的内在定量关系进行模拟。结果表明,闪点预测值与实验值符合良好。本方法的提出为工程上提出了一种预测二元互溶液体闪点的有效方法,可应用于评估混合溶液的火灾爆炸危害性及本质较安全设计。     

A prediction model for the flash point of binary liquid mixtures

A prediction model based on the partial least squares of the multivariate statistical analysis methods was developed for the flash point (FP) of binary liquid mixtures. Estimation of the FP of flammable substances is important for safety measures in industrial processes. Since experimental FP data of liquid mixtures are scarce in the literature, there have been many researches to estimate the FP of liquid mixtures using physicochemical laws. In this study, the partial least squares (PLS) method using experimental data was used as a prediction model of the FP of binary liquid mixtures. The FPs predicted from the PLS method were also compared to results from the existing calculating methods using physicochemical laws such as Raoult's law and the Van Laar equation.     

烃类沸点的定量构效关系研究

应用CODESSA软件计算296种烃类物质的分子结构描述符,分别用启发式回归（HM）和最佳多元线性回归（B-MLR）筛选计算出的所有分子描述符,并建立沸点的线性回归模型。用B-MLR方法筛选出的4个描述符作为支持向量机（SVM）的输入建立了非线性模型。预测结果表明：所建立的模型稳健,泛化能力强,预测误差小。非线性模型（R2=0.9905,RMSE=10.2295）的性能优于线性回归模型（HM：R2=0.9819,RMSE=14.0606;B-MLR：R2=0.9842,RMSE=13.1058）,预测效果令人满意。     

Flammability hazard analysis of imidazolium-based ionic liquid binary mixtures under high temperatures

Ionic liquid (IL) mixtures are promising because they can optimize the involved properties according to industrial needs. It has already been demonstrated that IL flammability is due mainly to IL decomposition generating flammable substances. Four different ILs, 1-Butylimidazolium tetrafluoroborate ([BIM][BF₄]), 1-butylimidazolium nitrate ([BIM][NO₃]), 1-butyl-3-methylimidazolium tetrafluoroborate([BMIM][BF₄]), and 1-butyl-3-methylimidazolium nitrate ([BMIM][NO₃]), were selected as the parent salts to form the different imidazolium-based IL binary mixtures. These mixtures were tested via isothermal thermogravimetric analyzer (TGA) at different temperatures (120, 150, 180, 210, and 240 °C), then tested by the flash point analyzer after isothermal heating pretreatment at the above temperatures. Results show that the mixtures' flash point values decrease with the heating temperature increase. Vaporization of the IL mixtures’ decomposition products results in a higher concentration of flammable gases and a flash point decrease, which lead to the flammability hazard increasing. Moreover, results show that the flash points of the studied binary imidazolium IL mixtures are more similar to those of the more unstable IL in their parent ILs. Also, the flammability hazard of IL binary mixtures may obviously increase under the high temperature environment for a long time.     

基于人工神经网络的二元液体闪点预测

建立了一个基于人工神经网络的理论模型,用于预测二元混合液体的闪点.根据所研究混合液体的物理性质,选择了相关黏度、表面张力等物理参数来表征闪点,以这些参数作为输入参数,二元混合液体的闪点作为输出值,应用反向传播(BP)人工神经网络方法对两者之间的内在定量关系进行模拟.结果表明,闪点预测值与实验值符合良好,优于传统的计算方...     

Flash point prediction for the binary mixture of phosphatic solvents and n-dodecane from UNIFAC group contribution model

The flash point temperature of Tri-n-butyl phosphate(TBP) and Tri-iso-amyl phosphate(TiAP) in n-dodecane binary mixture has been measured for the entire concentration range using the continuously closed cup flash point apparatus based on the ASTM D6450 method. The flash point was predicted using the UNIFAC group contribution model. The measured flash point was also compared with the prediction from the NRTL, UNIQUAC and Ideal solution models. The UNIFAC model is able to predict the flash point fairly well for the TBP–dodecane mixture and for TiAP–dodecane mixture no improvements is obtained over the ideal solution assumption. The flash point shows positive deviation from ideal solution behavior for both the binary mixture.     

基于分子结构的链烷烃辛烷值预测研究

基于定量结构-性质相关(QSPR)原理,研究化学物质的结构与性能之间的关系,应用遗传-偏最小二乘(GA -PLS)方法从大量结构参数中筛选出与链烷烃马达法辛烷值最相关的5个分子描述符,采用多元线性回归方法,建立了根据分子结构预测链烷烃马达法辛烷值的数学模型.结果表明,模型具有较高的稳定性以及预测能力.为工程上提供了一种...     

A mathematical model for predicting the flash point of binary solutions

A mathematical model which may be used for predicting the flash point of binary solutions has been proposed and subsequently verified by experimentally-derived data, such data pertaining to an almost-ideal solution as also to highly non-ideal solutions. The results reveal that the model is able to precisely predict the flash point over the entire composition range of binary solutions for both ideal solutions and non-ideal solutions by way of utilizing the flash point of the individual components. The highly non-ideal solution like octane+ethanol exhibits the minimum flash-point behavior, which leads to the minimum on the flash point vs composition curve.     

二元混合液体闪点的计算

以混合溶液纯组分易燃液体闪点的饱和蒸气压为基础,应用乌拉尔定律、双液系的气-液相平衡理论,运用Le Chatelier方程和安托因方程导出二元混合液的闪点计算方法。并例举易燃液体与易燃液体组成的理想混合液、易燃液体与易燃液体组成的非理想混合液、易燃液体与不燃液体组成的非理想混合液的计算过程。乙醇溶液闪点的计算结果与现有的文献资料比较,误差在允许范围内。计算数据用Excel处理,快捷准确,用于确定二元混合液体的火灾危险性。     

应用电性拓扑状态指数预测烷烃自燃点

建立了一个基于人工神经网络的定量结构-性质相关性模型,用于52种烷烃化合物自燃点的预测研究。应用原子类型电性拓扑状态指数作为表征分子结构特征的描述符。该指数既能表征分子的电子特性,又反映其拓扑特征,同时易于计算,并有较强的同分异构体区分能力。采用误差反向传播（BP）神经网络方法对烷烃自燃点与电性拓扑状态指数间可能存在的非线性关系进行拟合。将52种烷烃样本随机划分为训练集（30种）、验证集（8种）和测试集（14种）,并通过“试差法”确定网络的最优参数。运用最佳网络结构[64—1]对实验样本进行模拟,结果表明,多数样本的自燃点预测值与实验值符合良好,对于测试集,平均预测绝对误差为8．4℃,均方根误差为11．8,优于多元线性回归方法和传统基团贡献法所得结果。该方法的提出为工程上提供了一种根据分子结构预测有机物白燃点的有效方法。