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.     

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.     

导热性能对浸油沙层闪点、燃点的影响研究

研究了浸油沙层的导热性能对浸油沙层闪点和燃点的影响。选用柴油作为燃料液体,铜粉和沙子的混合物代表沙层,浸油沙层的导热系数由混合物中的沙子和铜粉比例控制。实验结果表明,浸油沙层的导热性能对其着火特性有重要影响。不加铜粉时,存在一个使混合物的闪点与燃点最小的沙油质量比。随着铜粉添加量的增加,闪点与燃点的最小值点逐渐消失。这可能是加入细铜粉使混合物的表面平整造成的。在最小点后,随铜粉添加量的增加,也就是随混合物的导热性能的改善,混合物的闪点与燃点增大。但当沙子与铜粉的质量比达到1：1时,闪点与燃点随沙油质量比的变化出现了一个最大值,之后闪点和燃点随沙油质量比的增加而降低。这些现象的出现可能是导热能力、毛细现象和混合物内部的空隙三者共同作用的结果。     

Nonlinear molecular based modeling of the flash point for application in inherently safer design

New chemical process design strategies utilizing computer-aided molecular design (CAMD) can provide significant improvements in process safety by designing chemicals with required target properties and the substitution of safer chemicals. An important aspect of this methodology concerns the prediction of properties given the molecular structure. This study utilizes one such emerging method for prediction of a hazardous property, flash point (FP), which is in the center of attention in safety studies. Using such a reliable data set comprising 1651 organic and inorganic chemicals, from 79 diverse material classes, and robust dynamic binary particle swarm optimization for the feature selection step resulted in the most efficient molecular features of the FP investigations. Apart from the simple yet precise five-parameter multivariate model, the FP nonlinear behavior was thoroughly investigated by a novel hybrid of particle swarm optimization and support vector regression. Besides, 195 missing experimental FPs of the DIPPR data set are predicted via the presented procedure.     

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.     

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.     

The lower explosion point—A good measure for explosion prevention: Experiment and calculation for pure compounds and some mixtures

It is well known that explosive mixtures can exist at temperatures below the flash point (FP). Experiments show that the difference between the FP and the lower explosion point (LEP) may be up to 15 K and in some special cases even more. Consequently, an industrial process even running a few Kelvin below the FP may operate in an explosive vapour/air-atmosphere. Operating at temperatures below the LEP gives sufficient safety. Calculated LEPs are discussed and compared with experimental results.     

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.     

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.     

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.     

A reappraisal of the flash point of formic acid

The currently accepted closed-cup flash points of four common organic compounds—formic acid, glycerol, ethylamine and dimethylamine—are examined for consistency with the principles of stoichiometry, vapour pressures and flammability limits. For all four, the examination suggests that the flash points are in significant error on the hazardous side.     

轻柴油储罐火灾过程及安全性评价

从 2 0 0 2年 1月 1日起我国实施新的“轻柴油”产品标准 ,新标准将轻柴油的闪点指标由原来的≥ 65℃改为≥ 5 5℃ ,由此导致轻柴油的火灾危险类别发生变化 ,并进而引发原有轻柴油储罐能否安全储存新标准轻柴油的问题。笔者利用化学流体力学模型和挪威船级社 (DNV)的SAFETI风险评估软件对 65℃闪点和 5 5℃闪点轻柴油的储存火灾危害性进行了评估 ,取得了轻柴油火灾中危害程度的计算机模拟结果     

环境因素对服装点对点电阻测试结果影响分析

静电放电过程中产生的电火花或电磁场在某些特定环境中会造成重大危害,引起社会广泛关注。穿戴防静电服是静电防护的重要手段,防静电服面料的点对点电阻是防静电服主要性能指标之一,对该指标的准确测量有助于对服装防护能力的准确认识。本文阐述了面料点对点电阻测试原理,详细讨论了防静电服点对点电阻测试结果的组成,指出测试过程中的影响因素包括环境温湿度、测试台面绝缘性能、仪器周围的电磁环境和操作人员的带电等,其中重点分析了环境温湿度对测试结果的影响,指出环境温湿度是点对点电阻测试过程中的主要影响因素,以期通过对相关因素的控制提高对该项性能指标测试结果的准确程度。     

外界辐射热对浸油沙层着火特性影响研究

通过实验研究了施加辐射热后浸油沙层的着火特性.实验中使用柴油为燃料,沙层为多孔介质,自制的施加辐射热测量闪点和燃点的设备,研究了施加辐射热后,沙油质量比、沙粒直径以及外界辐射热通量对浸油沙层着火特性的影响.结果表明,沙粒直径及沙油质量比不变,随着辐射热通量的增大,浸油沙层的闪点、燃点降低到某一值后不再变化;施加辐射热通量,浸油沙层的闪点、燃点随沙油质量比的增加而升高,不再出现闪点和燃点的最小值点;沙粒直径对浸油沙层的闪点和燃点有影响.     

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。     

细水雾灭油池火时各种影响因素的相对重要度分析

采用实验的方法并与理论分析相结合，对细水雾在扑灭B类火时，占主导地位的灭火机理进行了研究。实验中发现，当液体燃料的闪点相差较大时，其灭火机理往往不尽相同。并且当细水雾的雾特性相差较大时，在灭火中起主导作用的灭火机理常常也各有不同。因而那种单纯以雾滴的粒径分布作为细水雾扑灭燃料闪点较高的油池火时，雾滴的动量分布对细水雾的灭火有效性影响更大。为此，本文还建立了一个简单的模型，试图得到在这种火灾场景下，对细水雾灭火的临界动量要求。     

固体燃料烟点测量装置的研制

本文介绍了最近研制的一种用于固体燃料烟点测量的装置及配合烟点准确测定而开发的烟粒监测系统,并就PMMA材料进行了测试。本项研究为固体燃料的火焰高度及烟点的测量,首次提供了合理、可行的技术方法。     

Determination of emergency assembly point for industrial accidents with AHP analysis

Major industrial accidents, which are a type of technological disaster, are very important due to the security risks and financial damages that threaten the environment and human health in today's industrialization. In this study, it was aimed to propose an approach that will guide the decision makers to choose the emergency assembly point that should be in the distance or shelter where the employees will be not affected by the negative consequences of emergencies within the scope of the obligation of industrial establishments preparing an internal emergency plan for major industrial accidents. For this purpose, in the first stage, modelling studies were carried out with ALOHA (Areal Locations of Hazardous Atmospheres) Software over possible accident scenarios in an industrial establishment containing different types and amounts of hazardous chemicals. As a result of modelling studies, possible toxic emissions, fire and explosion effect distances and threat zones for the industrial establishment were obtained. In the second stage, the weights of the main and sub-selection criteria to be used in determining the assembly point were calculated. This stage was carried out based on the comparison data obtained as a result of the questionnaire applied to professionals with the help of AHP (Analytic Hierarchy Process) method, which is one of the multi-criteria decision making methods. In the last stage, three candidate points were selected considering the physical effect areas determined in the first stage in the boundaries of the establishment, where the employees were evaluated to be affected the minimum from the negative consequences of industrial accidents. These candidate points were evaluated again with the AHP method on the basis of the sub-criteria whose relative weights were determined in the second stage and a selection was made. As a result, an approach that provides the solution of our problem was obtained.