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。     

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.     

Binary liquid solutions exhibiting minimum flash-point behavior

The sufficient conditions for a binary liquid solution to behave as a minimum flash-point solution and the equations to determine the minimum flash-point composition and minimum flash-point are proposed here. Since these sufficient conditions and the associated equations were derived based upon the derivative of flash point with respect to composition (solution composition effect upon flash point), such a derivative equation was derived, initially, based upon the flash point-prediction model proposed in a previous study and then verified by the application of experimental data. Subsequently, this derived composition effect upon the flash-point equation for a liquid solution was applied to derive both the sufficient conditions and equations referred to above, with such conditions and equations both being verified by comparison of predicted results with experimentally derived data. The results reveal that these derived conditions are satisfactory for the identification of the status of a liquid solution as being a minimum flash-point solution and the proposed equations satisfactory to successfully estimate the value of the minimum flash-point composition and the minimum flash point.     

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.     

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.     

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...     

Experimental investigation on the lower flammability limits of diethyl ether/ n-pentane/epoxypropane-air mixtures

Diethyl ether (DEE), epoxypropane (PO) and n-pentane have excellent ignition and combustion performance; hence, they have a wide variety of applications in industry and advanced aviation propulsion systems. As these fuels are flammable at normal temperature and pressure, their explosive characteristics need to be explored. In this study, the lower flammability limits (LFLs) of vapor mixtures of DEE/PO/n-pentane in air were measured in 20 L, closed, stainless steel spherical vessels. Experimental results were obtained at ambient atmospheric pressure and an initial temperature of 40 °C. The experimental results show that the LFLs of DEE-air, n-pentane -air, and PO-air are 1.81 vol%, 1.41 vol% and 2.44 vol%, respectively. The LFLs of binary/ternary fuel mixtures under different compositions were tested, and the experimental results are compared with the classical Le Chatelier's formula. The results show that, for the binary fuels (i.e., DEE/PO, DEE/n-pentane, PO/n-pentane)-air mixtures, the maximum difference of the LFLs between Le Chatelier's formula and the experimental results is 6.10%. For the ternary fuels (i.e., DEE/PO/n-pentane)-air mixtures, the maximum difference of the LFLs between the two results is 6.33%. Due to the adiabatic flame temperature of each single fuel mixture being close, the Le Chatelier's formula is applicable for an estimation of the LFL for DEE/PO/n-pentane-air mixtures.     

Flame propagation through aluminum particle cloud in a combustion tube

The propagation of a flame is investigated experimentally and theoretically for a large, horizontal combustion tube containing a mixture of air and aluminum powder with pre-existing turbulence. One end of the tube is closed and the other is connected to a large dump-tank. Twenty dispersion systems are used on the tube to produce a uniform suspension of aluminum dust in the tube with a mean diameter of 6 μm. The characteristics of a flame front from the ignitors at the closed end are measured using photodiodes and the development of pressure is monitored by transducers. Experimental results revealed the entire process of an accelerating flame and the development of shock waves. A set of conservation equations for two-phase turbulent combustion flow is derived, using the two-fluid model, k–ε model, Hinze–Tchen model and EBU-Arrhenius model for turbulent combustion. The SIMPLE scheme usually applied to the homogeneous turbulent combustion is extended to fit this two-phase, reactive behavior. The results of calculations show the positive feedback coupling among combustion, expansion and turbulence during flame propagation. Computed and measured results are generally in good agreement.     

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.     

Experimental measurement and numerical analysis of binary hydrocarbon mixture flammability limits

The flammability limits of binary hydrocarbon mixtures in air were measured in a combustion apparatus using an innovative method developed for this apparatus. The experimental results were obtained at standard conditions (room temperature and ambient atmospheric pressure) with upward flame propagation. The experimentally determined flammability limits for pure hydrocarbons (methane and ethylene) were compared with existing data reported in the literature. Le Chatelier's Law was fit to all experimental data to obtain LFLs and UFLs for various two-component combinations of saturated and unsaturated hydrocarbons (methane, ethylene, acetylene, propane, propylene, and n-butane). A modification of this law was used if experimental observations showed large deviations from Le Chatelier's predictions. Also, experimentally measured flammability limit data of the binary hydrocarbon mixtures were analytically related to the stoichiometric concentrations.     

A generalized differential method to calculate lumped kinetic triplet of the nth order model for the global one-step heterogeneous reaction using TG data

Computing kinetic triplet is of importance for the process safety of combustion/gasification industries to establish the chemical reaction scheme and to assess the hazardous risk. Few approaches have been capable of calculating lumped kinetic triplet at one time efficiently, which might be attributed to the fact that the analytical solution for the nonlinear ordinary differential equation (NNODE) for the n^th order reaction model has not been found yet. This paper presents an analytical solution of NNODE to compute kinetic triplet. Results showed that the proposed method (mass fraction curve-fitting error ϕ = 1.49%–2.07%) is more efficient to compute kinetic triplet of the n^th order reaction model, comparing to genetic algorithm (GA) optimization (ϕ = 1.43%–1.81%), Coats-Redfern (ϕ = 2.36%–3.16%), peak-shape, and isoconversional methods. A compensation effect between lnA and E_a is observed due to heating rates. Effects of exported data quality and smooth processing on computation of kinetic triplet are discussed. It is the first time that an analytical solution of NNODE (n^th order model) for global one-step heterogeneous reaction is derived for computing kinetic triplet. This work may help to search for analytical solutions of power-law and Avrami-Erofeev models in the future to efficiently calculate kinetic triplet for accelerating and sigmoidal reaction systems.     

Prediction models for the flash point of pure components

The flash point for a liquid is defined as the lowest temperature at which its vapor forms a spontaneously ignitable mixture when brought in contact with air. Having a good estimation of the flash point temperature at normal conditions is relevant because it is one of the main properties used to characterize fire and explosion hazards for liquids. Most of predictive correlations in the literature include a physical effect by including the normal boiling temperature in their formulation. To achieve combustion, sufficient evaporation is required and then a part of the liberated energy from the combustion is used to support the evaporation. Thus the evaporation energy and a chemical effect given by the heat of reaction are incorporated in this work. It is firstly verified that the relation between the flash point temperature and the normal boiling temperature tends to be a constant. Thus a heuristic approach based on 611 chemical species of diverse families indicates that this relation is around 0.75. The dispersion of the error has been reduced by using two proposed correlations where both physical and chemical properties are included in the model. In particular, the second equation is based on the group contributions method, which has been developed for alkanes. This method is in fact a combination of the group contributions method and the first model to overcome the difficulties in predicting isomeric differences.     

Development of machine learning based prediction models for hazardous properties of chemical mixtures

Lower flammability limit (LFL), upper flammability limit (UFL), auto-ignition temperature (AIT) and flash point (FP) are crucial hazardous properties for fire and explosion hazards assessment and consequence analysis. In this study, a comprehensive prediction model set was constructed by using expanded chemical mixture databases of chemical mixture hazardous properties. Machine learning based gradient boosting quantitative structure-property relationship (GB-QSPR) method is implemented for the first time to improve the model performance and prediction accuracy. The result shows that all developed models have significantly higher accuracy than other regular QSPR models, with the 5-fold cross-validation RMSE of LFL, UFL, AIT, and FP models being 1.06, 1.14, 1.08, and 1.17, respectively. All developed QSPR models can be used to estimate reliable chemical mixture hazardous properties and provide useful guidance in chemical mixture hazard assessment and consequence analysis.     

Optimization of biodiesel production by alkali-catalyzed transesterification of used frying oil

Biodiesel as an alternative fuel for fossil diesel has many benefits such as reducing regulated air pollutants emissions, reducing greenhouse gases emissions, being renewable, biodegradable and non-toxic. In this study, used frying oil was applied as a low cost feedstock for biodiesel production by alkali-catalyzed transesterification. The design of experiments was performed using a double 5-level-4-factor central composite design coupled with response surface methodology in order to study the effect of factors on the yield of biodiesel and optimizing the reaction conditions. The factors studied were: reaction temperature, molar ratio of methanol to oil, catalyst concentration, reaction time and catalyst type (NaOH and KOH). A quadratic model was suggested for the prediction of the ester yield. The p-value for the model fell below 0.01 (F-value of 27.55). Also, the R² value of the model was 0.8831 which indicates the acceptable accuracy of the model. The optimum conditions were obtained as follows: reaction temperature of 65 °C, methanol to oil molar ratio of 9, NaOH concentration of 0.72% w/w, reaction time of 45 min and NaOH as the more effective catalyst. In these conditions the predicted and observed ester yields were 93.56% and 92.05%, respectively, which experimentally verified the accuracy of the model. The fuel properties of the biodiesel produced under optimum conditions, including density, kinetic viscosity, flash point, cloud and pour points were measured according to ASTM standard methods and found to be within specifications of EN 14214 and ASTM 6751 biodiesel standards.     

MNLR and ANN structural group contribution methods for predicting the flash point temperature of pure compounds in the transportation fuels range

A QSPR method is presented for predicting the flash point temperature (FPT) of pure compounds in the transportation fuels range. A structural group contribution method is used to determine the flash point temperature using two techniques: multivariable nonlinear regression and artificial neural networks. The method was used to probe the structural groups that have significant contribution to the overall FPT of pure compounds and arrive at the set of 37 atom-type structural groups that can best represent the flash point for about 375 substances. The input parameters to the model are the number of occurrence of each of the 37 structural groups in each molecule. The neural network method was the better of the two techniques and can predict the flash point of pure compounds merely from the knowledge of the molecular structure with an overall correlation coefficient of 0.996 and overall average and maximum errors of 1.12% and 6.62%, respectively. The results are compared to the more traditional approach of the SGC method along with other methods in the literature.     

Effects of a carbon monoxide-dominant gas mixture on the explosion and flame propagation behaviors of methane in air

This work aimed to experimentally evaluate the effects of a carbon monoxide-dominant gas mixture on the explosion characteristics of methane in air and report the results of an experimental study on explosion pressure measurement in closed vessel deflagration for a carbon monoxide-dominant gas mixture over its entire flammable range. Experiments were performed in a 20-L spherical explosion tank with a quartz glass window 110 mm in diameter using an electric spark (1 J) as the ignition source. All experiments were conducted at room temperature and at ambient pressure, with a relative humidity ranging from 52 to 73%. The peak explosion pressure (P_max), maximum pressure rise rate ((dp/dt)_max), and gas deflagration index (K_G) were observed and analyzed. The flame propagation behavior in the initial stage was recorded using a high-speed camera. The spherical outward flame front was determined on the basis of a canny method, from which the maximum flame propagation speed (S_n) was calculated. The results indicated that the existence of the mixture had a significant effect on the flame propagation of CH₄-air and increased its explosion risk. As the volume fraction of the mixed gas increases, the P_max, (dp/dt)_max, K_G and S_n of the fuel-lean CH₄-air mixture (7% CH₄-air mixture) increase nonlinearly. In contrast, addition of the mixed gas negatively affected the fuel-rich mixture (11% CH₄-air mixture), exhibiting a decreasing trend. Under stoichiometric conditions (9.5% CH₄-air mixture), the mixed gas slightly lowered P_max, (dp/dt)_max, K_G, and S_n. The P_max of CH₄-air mixtures at volume fractions of 7%, 9.5%, and 11% were 5.4, 6.9, and 6.8 bar, respectively. The S_n of CH₄-air mixtures at volume fractions of 7%, 9.5%, and 11% were 1.2 m/s, 2.0 m/s, and 1.8 m/s, respectively. The outcome of the study is comprehensive data that quantify the dependency of explosion severity parameters on the gas concentration. In the storage and transportation of flammable gases, the information is required to quantify the potential severity of an explosion, design vessels able to withstand an explosion and design explosion safety measures for installations handling this gas.     

多元可燃混合溶液的闪点预测

为了预测多元可燃混合液的闪点,建立了一个预测模型,通过二元和三元可燃混合液闪点实测值的试验对比,证实此模型的误差在可接受的范围内。此模型可以在预知可燃混合液组分比例和单组分闪点基础上快速预测混合液的闪点。