Prediction of the Cetane Number of Diesel Compounds Using the Quantitative Structure Property Relationship

Creton, Benoît; Dartiguelongue, Cyril; Bruin, Theodorus de; Toulhoat, H.

doi:10.1021/ef1008456

Cited by 74 publications

(70 citation statements)

References 16 publications

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“…Recently, we initiated a study to rationalize the formulation of alternative fuels using quantitative structure-property relationship (QSPR) approaches. We first developed models using machine learning approaches for pure component properties such as flash point (FP) [8], cetane number (CN) [8,9] and two temperature-dependent properties: density (ρ(T)) and viscosity (η(T)) [10]. For these properties, a large number of methods leading to linear and non-linear models have been applied, together with consensus modelling.…”

Section: Introductionmentioning

confidence: 99%

On the rational formulation of alternative fuels: melting point and net heat of combustion predictions for fuel compounds using machine learning methods

Saldana

Starck

Mougin

et al. 2013

SAR and QSAR in Environmental Research

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We report the development of predictive models for two fuel specifications: melting points (T(m)) and net heat of combustion (Δ(c)H). Compounds inside the scope of these models are those likely to be found in alternative fuels, i.e. hydrocarbons, alcohols and esters. Experimental T(m) and Δ(c)H values for these types of molecules have been gathered to generate a unique database. Various quantitative structure-property relationship (QSPR) approaches have been used to build models, ranging from methods leading to multi-linear models such as genetic function approximation (GFA), or partial least squares (PLS) to those leading to non-linear models such as feed-forward artificial neural networks (FFANN), general regression neural networks (GRNN), support vector machines (SVM), or graph machines. Except for the case of the graph machines method for which the only inputs are SMILES formulae, previously listed approaches working on molecular descriptors and functional group count descriptors were used to develop specific models for T(m) and Δ(c)H. For each property, the predictive models return slightly different responses for each molecular structure. Therefore, models labelled as 'consensus models' were built by averaging values computed with selected individual models. Predicted results were then compared with experimental data and with predictions of models in the literature.

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Section: Introductionmentioning

confidence: 99%

On the rational formulation of alternative fuels: melting point and net heat of combustion predictions for fuel compounds using machine learning methods

Saldana

Starck

Mougin

et al. 2013

SAR and QSAR in Environmental Research

View full text Add to dashboard Cite

show abstract

“…The database was built on the basis of experimental data gathered from different sources, such as the Design Institute for Physical Properties (DIPPR), database of chemical companies, the chemical database of the University of Akron (Ohio, USA) and the information available in Yaws' handbook [22][23][24][25][26][27]. It is important to notice the peculiarity of the CN database which was built on the basis of the compendium by Murphy et al [28] for oxygenated compounds and from Creton et al [29] for hydrocarbons. The local and complete database contains FP of 625 molecules, CN of 299 molecules, 5 634 experimental density values for 730 molecules and 3 547 experimental viscosity for 407 molecules.…”

Section: Methodsmentioning

confidence: 99%

Rational Formulation of Alternative Fuels using QSPR Methods: Application to Jet Fuels

Saldana

Creton

Mougin

et al. 2013

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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“…Though the model is accurate for the range of compounds considered, it is unable to predict the CN of compounds outside the test range. A recent model considered chemical families likely found in diesel fuels using the genetic function approximation (GFA), an iterative approach to generate relationships between molecular descriptors and CN [9]. Though the approach could not satisfactorily predict CN when including all 147 molecules in the data set, it utilized an approach of dividing the set into four different groups based on their chemical families to improve the model's predictive power.…”

Section: Predicting the Cetane Numbermentioning

confidence: 99%

“…ANN's were selected as the platform for prediction, as they have been shown to perform better than linear regression models and other group contribution methods in regards to predicting fuel properties [9].…”

Section: Neural Network Architecturementioning

confidence: 99%

Predicting the Cetane Number of Furanic Biofuel Candidates Using an Improved Artificial Neural Network Based on Molecular Structure

Kessler¹,

Sacia²,

Bell³

et al. 2017

Preprint

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TitlePredicting the cetane number of furanic biofuel candidates using an improved artificial neural network based on molecular structure b s t r a c tThe next generation of alternative fuels is being investigated through advanced chemical and biological production techniques for the purpose of finding suitable replacements for diesel and gasoline while lowering production costs and increasing process yields. Chemical conversion of biomass to fuels provides a plethora of pathways with a variety of fuel molecules, both novel and traditional, which may be targeted. In the search for new fuels, an initial, intuition-driven evaluation of fuel compounds with desired properties is required. Due to the high cost and significant production time needed to synthesize these materials at a scale sufficient for exhaustive testing, a predictive model would allow chemists to preemptively screen fuel properties of potentially desirable fuel candidates. Recent work has shown that predictive models, in this case artificial neural networks (ANN's) analyzing quantitative structure property relationships (QSPR's), can predict the cetane number (CN) of a proposed fuel molecule with relatively small error. A fuel's CN is a measure of its ignition quality, typically defined using prescribed ASTM standards and a cetane testing engine. Alternatively, the analogous derived cetane number (DCN), obtained using an Ignition Quality Tester (IQT), is a direct measurement alternative to the CN that uses an empirical inverse relationship to the ignition delay found in the constant volume combustion chamber apparatus. DCN data points acquired using an IQT were utilized for model validation and expansion of the experimental database used in this study. The present work improves on an existing model by optimizing the model architecture along with the key learning variables of the ANN and by making the model more generalizable to a wider variety of fuel candidate types, specifically the class of furans and furan derivatives, by including specific molecules for the model to incorporate. The new molecules considered include tetrahydrofuran, 2-methylfuran, 2-methyltetrahydrofuran, 5,5 0 -(furan-2-ylmethylene)bis(2-methyl furan), 5,5 0 -((tetrahydrofuran-2-yl)methylene)bis(2-methyltetrahydrofuran), tris(5-methylfuran-2-yl) methane, and tris(5-methyltetrahydrofuran-2-yl)methane. Model architecture adjustments improved the overall root-mean-square error (RMSE) of the base database predictions by 5.54%. Additionally, through the targeted database expansion, it is shown that the predicted cetane number of the furanbased molecules improves on average by 49.21% (3.74 CN units) and significantly for a few of the individual molecules. This indicates that a selected subset of representative molecules can be used to extend the model's predictive accuracy to new molecular classes. The approach, bolstered by the improvements http://dx

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Prediction of the Cetane Number of Diesel Compounds Using the Quantitative Structure Property Relationship

Cited by 74 publications

References 16 publications

On the rational formulation of alternative fuels: melting point and net heat of combustion predictions for fuel compounds using machine learning methods

On the rational formulation of alternative fuels: melting point and net heat of combustion predictions for fuel compounds using machine learning methods

Rational Formulation of Alternative Fuels using QSPR Methods: Application to Jet Fuels

Predicting the Cetane Number of Furanic Biofuel Candidates Using an Improved Artificial Neural Network Based on Molecular Structure

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