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

Saldana, Diego Alonso; Starck, Laurie; Mougin, Pascal; Rousseau, Bernard; Creton, Benoît

doi:10.1021/ef4005362

Cited by 58 publications

(62 citation statements)

References 37 publications

(114 reference statements)

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“…Saldana et al 51 obtained the same accuracy (MAE = 3.4°C) using a similar approach involving three support vector machine (SVM)-based QSPR models for predictions of the flash points, heats of vaporization, and boiling points of pure compounds that were introduced in a modified Liaw mixing rule.…”

Section: ■ Introductionmentioning

confidence: 82%

“…In recent work, Saldana et al 51 proposed the first QSPR model for the prediction of flash points of organic liquid mixtures. In this final model, the mole-weighted average values of the molecular descriptors of the pure compounds involved in the mixture were used in a genetic algorithm to develop a multilinear regression model with descriptors and an error in prediction of 10.1°C.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Indeed, the definition of mixture descriptors was identified as a critical parameter in existing QSPR models for mixtures, in particular for properties that can follow a nonlinear trend with the concentration of each component. Moreover, this study employed an extended database obtained by gathering additional data from the literature to enlarge the data set already collected and used by Saldana et al 51 from 287 to 435 data in order to extend the diversity of the compounds involved in the mixtures.…”

Section: ■ Introductionmentioning

confidence: 99%

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Mixture Descriptors toward the Development of Quantitative Structure–Property Relationship Models for the Flash Points of Organic Mixtures

Gaudin

Rotureau

Fayet

2015

Ind. Eng. Chem. Res.

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Quantitative structure−property relationships (QSPRs) are increasingly used for the prediction of physicochemical properties of pure compounds, but only a few have been developed to predict the properties of mixtures. In this work, a series of existing and new formulas were proposed to derive mixture descriptors for the development of QSPR models for mixtures. These mixture descriptors were used to model the flash points of a series of 435 organic mixture compositions. Multilinear models were obtained using 12 different mathematical formulas, taking into account the linear or nonlinear dependence of the flash point on the concentration of each compound. The best model, issued from the newly proposed (x 1 d 1 + x 2 d 2 ) 2 formula, was a four-parameter model presenting good prediction capabilities (with a mean absolute error in prediction of 10.3°C) compared with existing predictive methods for both mixtures and pure compounds. ■ INTRODUCTIONQuantitative structure−property relationships (QSPRs) are predictive models that allow the prediction of macroscopic properties by correlation with descriptors of the molecular structures of chemicals. 1 These molecular descriptors belong to various categories: 1,2 constitutional, topological, geometrical, or quantum-chemical. Such methods have been largely used for biological activities in the fields of toxicology, 3 ecotoxicology, 4 and pharmaceutics 5,6 and are increasingly used for physicochemical properties. 7−9Various models have been developed for hazardous physicochemical properties 10−19 such as flammability, 10,11 thermal stability, 12−14 and explosibility. 15−19 To date, the QSPR approach has mostly been dedicated to pure compounds, and only a few recent works have been dedicated to mixtures. 20 Ajmani and co-workers proposed various models to predict the densities 21,22 and infinite-dilution activity coefficients 23 of binary mixtures. In these studies, the molecular descriptors for each pure compound were combined, e.g., by mole-weighted averaging, 24 to derive mixture descriptors. These mixture descriptors were then correlated to the property of the studied mixtures. Several studies have also been dedicated to azeotropic mixtures. 25−29 In particular, Oprisiu et al. 29 developed several QSPR models to predict the boiling points 25,28 of azeotropic binary mixtures on the basis of fragment descriptors.The flash point (FP) is the temperature at which the vapor above a flammable liquid ignites under the effect of a spark. 30 This property characterizes flammability hazards of liquids and is a key safety issue in the risk assessment of industrial processes and in various regulatory frameworks dedicated to chemicals (for use, storage, and transport). 31,32 The flash points of pure compounds have been studied in several works with the aim of developing predictive methods taking advantage of the large availability of data. 33,34 Among them, many are based on knowledge of other properties such as the boiling point. 24,35,36 The highest performance was obtained by Carroll ...

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Section: ■ Introductionmentioning

confidence: 82%

Section: ■ Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

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Mixture Descriptors toward the Development of Quantitative Structure–Property Relationship Models for the Flash Points of Organic Mixtures

Gaudin

Rotureau

Fayet

2015

Ind. Eng. Chem. Res.

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“…It is thus necessary to extend these models to the case of mixtures before any envisaged use during alternative fuel formulations. Various approaches were considered to predict flash point values of mixtures, and the most promising was obtained combining thermodynamics based model together with predictions of QSPRs . Available experimental data for mixture FPs are scarce, and our database comprised 287 data points for 25 mixtures (21 binary and four ternary mixtures) including 21 pure compounds (6 hydrocarbons and 15 oxygenated compounds).…”

Section: Applications In the Fields Of Energy Transport And Environmentioning

confidence: 99%

Chemoinformatics at IFP Energies Nouvelles: Applications in the Fields of Energy, Transport, and Environment

Creton

2017

Molecular Informatics

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The objective of the present paper is to summarize chemoinformatics based research, and more precisely, the development of quantitative structure property relationships performed at IFP Energies nouvelles (IFPEN) during the last decade. A special focus is proposed on research activities performed in the "Thermodynamics and Molecular Simulation" department, i. e. the use of multiscale molecular simulation methods in responses to projects. Molecular simulation techniques can be envisaged to supplement dataset when experimental information lacks, thus the review includes a section dedicated to molecular simulation codes, development of intermolecular potentials, and some of their possible applications. Know-how and feedback from our experiences in terms of machine learning application for thermophysical property predictions are included in a section dealing with methodological aspects. The generic character of chemoinformatics is emphasized through applications in the fields of energy, transport, and environment, with illustrations for three IFPEN business units: "Transports", "Energy Resources", and "Processes". More precisely, the review focus on different challenges such as the prediction of properties for alternative fuels, the prediction of fuel compatibility with polymeric materials, the prediction of properties for surfactants usable in chemical enhanced oil recovery, and the prediction of guest-host interactions between gases and nanoporous materials in the frame of carbon dioxide capture or gas separation activities.

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“…Such a simple representation of compounds has been shown to provide relevant descriptors for QSPR modeling. 16,17,18,19 Descriptors used in this study (X1 to X38) are given in Table 2. FGCD from X19 to X38 named as "Me2Me", i.e.…”

Section: Descriptors For Mixturesmentioning

confidence: 99%

Prediction of Optimal Salinities for Surfactant Formulations Using a Quantitative Structure–Property Relationships Approach

Muller

Maldonado

Varnek³

et al. 2015

Energy Fuels

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Each oil reservoir could be characterized by a set of parameters such as temperature, pressure, oil composition, and brine salinity, etc. In the context of the chemical enhanced oil recovery (EOR), the selection of high performance surfactants is a challenging and time-consuming task since this strongly depends on the reservoir’s conditions. The situation becomes even more complicated if the surfactant formulation is a blend of two or more surfactants. In the present work, we report quantitative structure–property relationships (QSPR) correlating surfactants’ structures and their composition in a mixture with optimal salinity (S opt), corresponding to minimal interfacial tension in the reference brine/surfactants/n-dodecane system, at T = 313 K and P = 0.1 MPa. Particular attention was paid to selected families of surfactants: α-olefin sulfonate (AOS), internal olefin sulfonate (IOS), alkyl ether sulfate (AES), and alkyl glyceryl ether sulfonate (AGES). The models were built and validated on the database containing S opt values for 75 surfactants’ formulations. Molecular structures of amphiphilic molecules were encoded by functional group count descriptors (FGCD), ISIDA substructural molecular fragment (SMF) descriptors, and CODESSA molecular descriptors (CMD). For mixtures, descriptors were calculated as linear combinations of descriptors of individual compounds weighted by their mass fractions in mixtures. Different machine-learning methodssupport vector machine (SVM), partial least-squares (PLS) regression, and random subspace (RS)have been used for the modeling. Both global (on the entire database) and local (on individual families) models have been built. Models display reasonable accuracy (about 0.2 log S opt units) which is comparable with the experimental error of measured S opt. Our results show that the suggested approach can be successfully used to build predictive models for relatively small data sets of mixtures of chemical compounds.

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

Cited by 58 publications

References 37 publications

Mixture Descriptors toward the Development of Quantitative Structure–Property Relationship Models for the Flash Points of Organic Mixtures

Mixture Descriptors toward the Development of Quantitative Structure–Property Relationship Models for the Flash Points of Organic Mixtures

Chemoinformatics at IFP Energies Nouvelles: Applications in the Fields of Energy, Transport, and Environment

Prediction of Optimal Salinities for Surfactant Formulations Using a Quantitative Structure–Property Relationships Approach

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