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

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

doi:10.2516/ogst/2012034

Cited by 17 publications

(18 citation statements)

References 15 publications

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“…At the same time, advances in applying quantitative structure property relationship (QSPR) and quantitative structure activity relationship (QSAR) techniques have led to improved predictions of cetane number [143][144][145], flashpoint [144], density and viscosity [146], melting point [147], heat of combustion [147], and laminar burning velocities of biofuels [148]. Recently, Saldana et al [149] have suggested QSPR based methods for formulating alternative fuels for specific applications.…”

Section: Advances In Chemical Analysis Methodsmentioning

confidence: 99%

Chemical kinetic and combustion characteristics of transportation fuels

Dryer

2015

Proceedings of the Combustion Institute

165

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Internal combustion engines running on liquid fuels will remain the dominant prime movers for road and air transportation for decades, probably for most of this century. The world's appetite for liquid transportation fuels derived from petroleum and other fossil resources is already immense, will grow, will at some future time become economically unsustainable, and will become infeasible only in the very long term. The ongoing process of augmenting and eventually replacing petroleum-derived fuels with liquid alternative fuels must necessarily involve approaches that result in comparatively much lower net carbon cycle emissions from the transportation sector, most likely through a combination of carbon sequestration and renewable fuel production. The successful growth and establishment of a sustainable, profitable alternative fuels industry will be best facilitated by approaches that integrate alternative products into petroleum-derived fuel streams (i.e., gasolines, diesel, and jet fuels) and consider synergistic evolution of and integration with prevailing refining and liquid fuel distribution infrastructures. The emergence of low temperature combustion strategies, particularly those implementing dual fuel methods to achieve Reaction Controlled Compression Ignition (RCCI), offers the potential to significantly improve operating efficiency and reduce emissions with minimal aftertreatment. For all advanced combustion engine technologies, but especially for RCCI, a clear understanding of fuel property influences on combustion behaviors will be important to achieving projected engine performance and emissions. To achieve the benefits projected by emerging engine technologies, the properties of petroleumderived fuels themselves must be modified over time, but the effects of blending candidate alternative fuels with these conventional fuels must also be understood. Predicting the coupled physical and chemical property effects of real fuels on energy conversion system performance and emissions is a daunting problem, even for petroleum-derived real fuels, since each is composed of several hundred to thousands of individual chemical species typically belonging to one of a few organic classes (e.g., n-paraffins, isoparaffins, cyclo-paraffins, olefins, aromatics). For specific combustion applications, it is often the global combustion response to variations in the composition of fuel mixtures-inclusive of those occurring by blending petroleum-derived fuel with alternative fuel candidates-that is of interest for fuel property optimization. This paper presents an overview of tools used for evaluating and emulating combustionrelevant properties of real fuels and alternative fuel candidates. New analytical and statistical methods can provide important insights as to how the ensembles of distinct molecular structures found in a given fuel mixture contribute to the physical and chemical kinetic properties that govern its combustion in energy conversion processes. Such tools can in turn assist in screening candidate alternative f...

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Section: Advances In Chemical Analysis Methodsmentioning

confidence: 99%

Chemical kinetic and combustion characteristics of transportation fuels

Dryer

2015

Proceedings of the Combustion Institute

165

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“…A series of QSPR based models have been developed focusing on some fuel specification requirements which consist in stringent limits for various thermophysical properties. [73,74] During these studies, properties under consideration were the following: cetane number (CN), [75,76] flash point (FP), [76,77] melting point (T m ), [78] net heat of combustion (D c H), [78] and two temperature dependent properties the density (1(T)), [79] and viscosity (h(T)). [79] Table 1 gathers results from our previous works presenting characteristics of obtained consensus models for these six properties.…”

Section: Fuel Properties and Compatibility With Materialsmentioning

confidence: 99%

“…Chemoinformatics tools were early identified by our group to meet expectations of fast and accurate models. A series of QSPR based models have been developed focusing on some fuel specification requirements which consist in stringent limits for various thermophysical properties . During these studies, properties under consideration were the following: cetane number (CN), flash point (FP), melting point (T m ), net heat of combustion (Δ c H), and two temperature dependent properties the density (ρ(T)), and viscosity (η(T)) .…”

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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“…The QSPR model involving in a database of 212 compounds developed by Kauffmann et al coupled multiple linear regression with computational neural network methods to calculate liquid viscosities. Saldana et al − developed some QSPR models to predict liquid densities, viscosities, flash points, and cetane number of pure compounds as well as flash points of fuel mixtures. The models used functional groups and molecular descriptors to estimate these physical properties through machine-learning methods and obtained good results.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure–Property Relationship Model for Hydrocarbon Liquid Viscosity Prediction

et al. 2018

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The liquid viscosity of hydrocarbon compounds is essential in the chemical engineering process design and optimization. In this paper, we developed a quantitative structure−property relationship (QSPR) model to predict the hydrocarbon viscosity at different temperatures from the chemical structure. We collected viscosity data at different temperatures of 261 hydrocarbon compounds (C 3 −C 64 ), covering n-paraffins, isoparaffins, olefins, alkynes, monocyclic and polycyclic cycloalkanes, and aromatics. We regressed the experimental data using an improved Andrade equation at first. Hydrocarbon viscosity versus temperature curves were characterized by only two parameters (named B and T 0 ). The QSPR model was then built to capture the complex dependence of the Andrade equation parameters upon the chemical structures. A total of 36 key chemical features (including 15 basic groups, 20 united groups, and molecular weights) were manually selected through the trialand-error process. An artificial neural network was trained to correlate the Andrade model parameters to the selected chemical features. The average relative errors for B and T 0 predictions are 2.87 and 1.05%, respectively. The viscosity versus temperature profile was calculated from the predicted Andrade model parameters, reaching the mean absolute error at a value of 0.10 mPa s. We also proved that the established QSPR model can describe the viscosity versus temperature profile of different isomers, such as isoparaffins, with different branch degrees and aromatic hydrocarbons with different substituent positions. At last, we applied the QSPR model to predict gasoline and diesel viscosities based on the measured molecular composition. A good agreement was observed between predicted and experimental data (absolute mean deviation equals 0.21 mPa s), demonstrating that it has capacity to calculate viscosity of hydrocarbon mixtures.

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

Cited by 17 publications

References 15 publications

Chemical kinetic and combustion characteristics of transportation fuels

Chemical kinetic and combustion characteristics of transportation fuels

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

Quantitative Structure–Property Relationship Model for Hydrocarbon Liquid Viscosity Prediction

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