Prediction Of Flash Point Research Articles

Calculation and prediction of binary mixture flash point using correlative and predictive local composition models

Flash point (FP) is a key property and play an important role in hazard classification, safe handling, transportation, and it is used to characterize the fire hazards of liquids. The prediction of this important property of mixtures, leads to design process to avoid the occurrence of fire or explosion. Activity coefficient models are vital to impose the non-ideality of the mixture and accurately predict the flash point of the system. In the present work, correlative local composition activity coefficient models such as Wislon, NRTL, UNIQUAC, and predictive models such as UNIFAC, UNIFAC-LBY, UNIFAC-DMD, and NIST modified UNIFAC (UNIFAC-NIST) together with the general flash point model of Liaw et al. were used to predict the flash point of 15 binary mixtures. The deviations on the flash point using the Ideal, Wilson, NRTL, UNIQUAC, UNIFAC, UNIFAC-LBY, UNIFAC-DMD, and UNIFAC-NIST, are 1.68, 1.72, 1.73, 1.80, 2.04, 2.11, 2.36, and 4.02 K, respectively. Finally, one can conclude that the results of predictive model are superior respect to the correlative models. UNIFAC-LBY model gives the minimum deviation amongst the predictive models, even rather than UNIFAC-NIST model, which in it development, the quality of experimental data critically evaluated. So, UNIFAC-LBY model successfully can be used to predict the unreported FP data, and those materials that are toxic, explosive, and radioactive. Finally, in the absence of phase equilibria data, the local composition parameters regressed through the flash point data.

Prediction of flash points of pure organic compounds: Evaluation of the DIPPR database

The flash point is one of the most important flammability properties of compounds for the design of inherently safe processes. Many models have been developed to predict the flash point using the DIPPR database. However, for only 740 of the 1628 organic compounds available in the DIPPR database, the data for both flash point and normal boiling point were experimentally determined. For the other compounds, at least one of these properties was predicted and therefore is not appropriate for model development. The present study introduces a model to predict the flash points of pure organic compounds using their molecular structures and normal boiling points. The new model exploits the equality of the relative errors observed for the normal boiling point and flash point values predicted using the Joback method. Consequently, the relative error of the predicted normal boiling points can be used as a scaling factor to modify the predicted flash points. The ability of the model to evaluate the accuracy of a database was investigated. The ratio of the relative error of the predicted flash point to the relative error of the predicted normal boiling point obtained using the Joback method was proposed as a measure to evaluate the accuracy of flash point data.

Flash point prediction of tailor-made green diesel blends containing B5 palm oil biodiesel and alcohol

The flash point prediction accuracy of the Liaw model through UNIFAC-type models was evaluated and improved for B5 palm oil biodiesel–alcohol blends. The UNIFAC group interaction parameters of alcohol–alkyl chains (OH–CH2), alcohol–double bonded alkyl chains (OH–CC) and alcohol–esters group (OH–CCOO) were revised comprehensively to attend this improvement. The prediction accuracies (calculated as average absolute relative deviation (AARD)) were improved by solely revising the interaction parameters of OH–CH2 with similar AARD for all UNIFAC type models (from 7.0% and 5.8% to 1.1% for Original and NIST UNIFAC; from 6.6% and 6.2% to 1.3% for Modified UNIFAC (Dortmund) and NIST-Modified UNIFAC). The revised parameters were further validated using the test set data of B5-alcohol blends and B5-ethyl levulinate (EL)–butanol (BU) blends. Satisfactory improvements were obtained; but for B5–EL–BU blends, only the Original UNIFAC and NIST-UNIFAC model showed improvement. The presented study showed that by solely revising the interaction parameters of OH–CH3, a better flash point prediction is gained for the green diesel blends containing palm oil biodiesel and alcohol.

A simple and reliable method for prediction of flash point of alcohols based on their elemental composition and structural parameters

Flash point (FP) is an important parameter in hazard classification, safe handling, transportation, and storage of flammable liquids. On the basis of 929 experimental FPs of different alcohols, which were collected from different sources, a new correlation was developed for the estimation of FPs of alcohols. Different alcohols and phenols with various molecular structures were studied, that is, acyclic and cyclic alcohols as well as phenols and alcohols with composite aliphatic–aromatic structures. The new correlation is based on the elemental composition and some structural parameters, such as intermolecular hydrogen bonding, which can be found from the chemical structure of any type of alcohol. The root–mean–square deviation of the new model for external validation data set containing 164 compounds is 16.4K. Absolute percent error of the new model is >10% in only 24 alcohols and <5% in 714 alcohols. As the measured FPs reported in the literature sometimes differ in tens of degrees, the new simple model was compared with one of the best available predictive methods associated with much more reliable results with maximum errors less than approximately 70K.

Flash point prediction of the binary and ternary systems using the different local composition activity coefficient models

The flash point is an important physical property which is used to estimate the fire hazard of a flammable liquid. The prediction of this property for single and multi-components systems allows one to avoid the occurrence of fire or explosion. The several models have been developed for prediction of the flash point of the multi-component mixtures so far. In the present work, the activity coefficient models of Wilson, NRTL, UNIQUAC, and NRTL-NRF are used through general flash-point model of Liaw et al. to predict the flash point of the binary and ternary systems. In most cases due to lack of experimental binary vapor–liquid equilibrium data, the experimental closed cup flash point data are used to predict the binary interaction parameters in the activity coefficient functions. Moreover, the binary interaction parameters are used to predict the flash point of the ternary systems. Finally, one can conclude the results of NRTL model is superior respect to the other local composition models.

A Modified Group Contribution Method for Accurate Prediction of Flash Points of Pure Organic Compounds

Flash point is one of the most widely used properties in the risk assessment and safe design of process industries. In the current work, we have presented a novel and accurate model to predict the flash points of pure organic compounds from diverse families. The proposed model is a linear correlation between the flash point, normal boiling point, and 42 predefined functional groups constituting the molecule. Evaluation of the model through a data set of 1533 pure organic compounds shows an average absolute deviation, an average absolute relative error, and a correlation coefficient of 5.83, 1.61, and 0.992, respectively. Comparing the results of the present study with other works shows that the model proposed in this work is among the most accurate and reliable ones to date.

HATR–FTIR wavenumber selection for predicting biodiesel/diesel blends flash point

A novel HATR–FTIR wavenumber selection framework is proposed to predict the flash point of biodiesel/diesel blends. Partial Least Squares (PLS) regression is applied to spectra and four wavenumber importance indices are derived from PLS parameters. Noisy and irrelevant wavenumbers are then iteratively removed from the HATR–FTIR spectra according to the order suggested by each index following a backward procedure, and the Root Mean Square Error (RMSE) of the PLS model assessed. Two approaches are then suggested to select the recommended wavenumber subset once the iterative elimination procedure is finished. Using the recommended wavenumber importance index, the proposed method retained only average 5.13% of original wavenumbers, while reducing the average RMSE 21.6%, from 1.302 to 1.021. The method is then compared to flash point prediction with Principal Component Regression (PCR) when wavenumbers are selected using importance indices derived from Principal Component Analysis (PCA) parameters.

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.

Flashpoint Prediction for Binary Mixtures of Alcohols with Water in Order to Improve their Safety

AbstractAqueous solutions of alcohols are of considerable interest for a wide range of scientists and technologists. Simple dimensionless experimental formulae based on rational reciprocal and polynomial functions are proposed for correlation of the flashpoint data of binary mixtures of two components. The formulae are based on data obtained from flashpoint experiments and predictions. The main results are the derived flashpoint approximated values and the model verification by comparing the predicted values with the experimental data for the studied mixtures. Potential application for the model concerns the assessment of fire and explosion hazards, and the development of inherently safer designs for chemical processes containing binary partially miscible mixtures of an aqueous‐organic system.

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.

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.

Illuminating Flash Point: Comprehensive Prediction Models

Flash point is an important property of chemical compounds that is widely used to evaluate flammability hazard. However, there is often a significant gap between the demand for experimental flash point data and their availability. Furthermore, the determination of flash point is difficult and costly, particularly for some toxic, explosive, or radioactive compounds. The development of a reliable and widely applicable method to predict flash point is therefore essential. In this paper, the construction of a quantitative structure - property relationship model with excellent performance and domain of applicability is reported. It uses the largest data set to date of 9399 chemically diverse compounds, with flash point spanning from less than -130 °C to over 900 °C. The model employs only computed parameters, eliminating the need for experimental data that some earlier computational models required. The model allows accurate prediction of flash point for a broad range of compounds that are unavailable or not yet synthesized. This single model with a very broad range of chemical and flash point applicability will allow accurate predictions of this important property to be made for a broad range of new materials.

A Review of Flash Point Prediction Models for Flammable Liquid Mixtures

Flash point has safety implications and is therefore used to ascertain associated explosion hazards and fire of a flammable solution. Technological advances in the synthesis of new blends and chemical waste handlers have created a high demand for the flash point database and the flash point estimation methods of flammable liquid mixtures have become important. The present study reviewed the estimation model of the flammable liquid mixture flash point. These models are based on the following parameters: (1) either a normal boiling point or a composition range, (2) molecular structure (molecular descriptors), and (3) vapor pressure. Models based on boiling points or the composition ranges are empirically obtained using a mathematical regression method or an artificial neural network (ANN) approach. The quantitative structure–property relationship (QSPR) method is used to analyze the relationship between the flash point and the molecular structures that exist in a flammable mixture. Vapor-pressure-based mode...

Flash-point estimation for binary partially miscible mixtures of flammable solvents by UNIFAC group contribution methods

The flash points of binary partially miscible mixtures of flammable solvents were estimated using the flash point prediction model of Liaw et al. (2010) [18] handling liquid phase non-ideality through UNIFAC-type models, which do not need experimentally regressed binary parameters. This predictive model was validated by the experimental data over the entire composition range of seven mixtures. The model described the experimental flash point data well, with the exception of the region close to the span of the two-liquid-phase region nearing the high boiling-point compound of the mixtures because the estimation error in such a span resulted from poor predictive capability of the modified LLE UNIFAC parameters when using VLLE models to estimate the activity coefficient. Because of nonexistence of the disadvantage of VLLE model and superior predictive capability, a model based upon the VLE UNIFAC parameters provided accurate flash point predictions for binary partially miscible mixtures of flammable solvents, as revealed by comparison between predicted and experimental data. In addition, the flash point behaviors for two different types of partially miscible mixtures of flammable solvents are discussed.

Prediction of Physico-Chemical Properties for REACH Based on QSPR Models

Quantitative Structure Property Relationship models have been developed for the prediction of flash points of two families of organic compounds selected in the PREDIMOL French Project: amines and organic peroxides. If the model dedicated to amines respected all OECD validation principles with excellent performances in predictivity, the one dedicated to organic peroxides was not validated on an external validation set, due to the low number of available data, but already presented high performances in fitting and robustness. This work highlighted the need of gathering experimental data, as in progress in the PREDIMOL project, to achieve validated reliable models that could be used in a regulatory framework, like REACH. Such models are expected to be submitted to the European Joint Research Comity (JRC) and to existing tools (like the OECD ECHA QSAR Toolbox) to be available for use by industrials and regulatory instances.

Quantitative Structure-Property Relationship Study for Prediction of Flash Point of Some Organic Compounds Based On SW-MLR Method

A quantitative structure-property relationship (QSPR) model was developed for prediction of logFP values of some of organic compounds based on their molecular descriptors. In this study, we have attempted to develop a simple and fast multiple linear regression (MLR) model with high accuracy and precision. The molecular descriptors, which cover different information of molecular structures, were calculated by Dragon software. The most feasible descriptors were selected using stepwise selection. The QSPR model was validated by external set compounds without any contribution in model development step. The accuracy of the suggested model is illustrated using cross-validation, validation through an external test set and Y-randomization. The obtained multivariate linear three descriptors model by MLR has correlation coefficient (R2=0.905, Q2LGO = 0.844, Q2LGO=0.833, REP%=-7.807, F=47.626). All molecular descriptors which have entered in this model are calculated from chemical structure of any molecule. This model was used for the prediction of the flash point values of some organic compounds which were not used in the modeling procedure.

Prediction of Flash Points for Fuel Mixtures Using Machine Learning and a Novel Equation

In this work, a set of computationally efficient, yet accurate, methods to predict flash points of fuel mixtures based solely on their chemical structures and mole fractions was developed. Two approaches were tested using data obtained from the existing literature: (1) machine learning directly applied to mixture flash point data (the mixture QSPR approach) using additive descriptors and (2) machine learning applied to pure compound properties (the QSPR approach) in combination with Le Chatelier rule based calculations. It was found that the second method performs better than the first with the available databank and for the target application. We proposed a novel equation, and we evaluated the performance of the resulting, fully predictive, Le Chatelier rule based approach on new experimental data of surrogate jet and diesel fuels, yielding excellent results. We predicted the variation in flash point of diesel–gasoline blends with increasing proportions of gasoline.

A Quantitative Structure Property Relationship for Prediction of Flash Point of Alkanes Using Molecular Connectivity Indices

Many structure-property/activity studies use graph theoretical indices, which are based on the topological properties of a molecule viewed as a graph. Since topological indices can be derived directly from the molecular structure without any experimental effort, they provide a simple and straightforward method for property prediction. In this work the flash point of alkanes was modeled by a set of molecular connectivity indices (χ), modified molecular connectivity indices (mχht) and valance molecular connectivity indices (mχv), with mχv calculated using the hydrogen perturbation. A stepwise Multiple Linear Regression (MLR) method was used to select the best indices. The predicted flash points are in good agreement with the experimental data, with the average absolute deviation 4.3 K.

Flash points of partially miscible aqueous–organic mixtures predicted by UNIFAC group contribution methods

This work predicts the flash points of partially miscible aqueous–organic mixtures using the flash point prediction model of Liaw et al. (J. Chem. Eng. Data 55 (2010) 3451–3461) handling non-ideal behavior through liquid phase activity coefficients evaluated with UNIFAC-type models, which do not need experimentally regressed binary parameters. Validation of this entirely predictive model is conclusive with the experimental data over the entire flammable composition range for 10 aqueous–organic binary and ternary mixtures. Overall, the model describes the experimental data of flash point well when using UNIFAC-type models to estimate the activity coefficient. If the LLE UNIFAC parameters are not accessible, a model based upon the VLE UNIFAC parameters provides an acceptable means of predicting flash point for partially miscible aqueous–organic mixtures, as revealed by a comparison between predicted and experimental data.

Measurement and prediction of binary mixture flash point

Abstract The flash points of three binary mixtures, containing n-heptane, o-xylene, m-xylene and ethylbenzene, were measured by Pensky-Martens closed cup tester. The experimental data were compared with the calculated values using Liaw’s Model with the application of Raoult’s Law and Wilson equation. These equations were in good qualitative agreement.