On estimating physical and chemical properties of hydrocarbon fuels using mid-infrared FTIR spectra and regularized linear models

Wang, Yu; Ding, Yiming; Wei, Wei; Cao, Yi; Davidson, David F.; Hanson, Ronald K.

doi:10.1016/j.fuel.2019.115715

Cited by 41 publications

(17 citation statements)

References 46 publications

(57 reference statements)

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“…The model developed herein operates under the assumption that the composite spectra of a mixture can be calculated as the molar sum of the spectra of the pure components. This is a reasonable assumption for gas-phase spectra [38], in contrast to liquid spectra where excess absorbance has been reported previously, particularly for ethanol blends [39].…”

Section: Recent Efforts To Predict Physical and Chemical Properties Osupporting

confidence: 75%

Octane Prediction from Infrared Spectroscopic Data

Ibrahim

Farooq

2019

Energy Fuels

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A model for the prediction of research octane number (RON) and motor octane number (MON) of hydrocarbon mixtures and gasoline-ethanol blends has been developed based on infrared spectroscopy data of pure components. Infrared spectra for 61 neat hydrocarbon species were used to generate spectra of 148 hydrocarbon blends by averaging the spectra of their pure components on a molar basis. The spectra of 38 FACE (Fuels for Advanced Combustion Engines) gasoline blends were calculated using PIONA (Paraffin, Isoparaffin, Olefin, Naphthene, and Aromatic) class averages of the pure components. The study sheds light on the significance of dimensional reduction of spectra and shows how it can be used to extract scores with linear correlations to the following important features: molecular weight, paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic-CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, ethanolic OH groups, and branching index. Both scores and features can be used as input to predict octane numbers through nonlinear regression. Artificial Neural Network (ANN) was found to be the optimal method where the mean absolute error on a randomly selected test set was within the experimental uncertainty of RON, MON, and octane sensitivity.

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Section: Recent Efforts To Predict Physical and Chemical Properties Osupporting

confidence: 75%

Octane Prediction from Infrared Spectroscopic Data

Ibrahim

Farooq

2019

Energy Fuels

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“…[22] The advantages make ML play an important role in studies related to combustion theory, such as Computational Fluid Dynamics (CFD) simulation of combustion, [23][24][25] prediction of combustion phenomenon [26][27][28][29] and fuels. [30][31][32][33][34][35] ML can help to reduce mechanism scales, [23] optimize probability density function (PDF) [24] and Large Eddy Simulation (LES) [25] method in combustion simulation. When investigating combustion phenomenon, ML algorithms were used to detect thermoacoustic combustion oscillations, [26,27] distinguish donation and ignition [28] and reconstruct the detonation surface.…”

Section: Theoretical Researchmentioning

confidence: 99%

“…When investigating combustion phenomenon, ML algorithms were used to detect thermoacoustic combustion oscillations, [26,27] distinguish donation and ignition [28] and reconstruct the detonation surface. [29] In various fuel studies, ML also played an important role in the investigations about fuel properties, [30][31][32] the bio- [33,34] and next generation [35] fuels.…”

Section: Theoretical Researchmentioning

confidence: 99%

“…In the context of materials, ML techniques, a new tool in synthetic chemistry, [40] are often used for property prediction, seeking to learn a function that maps a molecular material to the property of choice. [38] Wang et al [30] built the correlations between mid-Infrared Fourier Transform Infrared (mid-IR FTIR) absorption spectra and physical and chemical properties for 64 hydrocarbon fuels. The least absolute shrinkage and selection operator (LASSO) regularized linear model, which takes FTIR spectra as input parameters, was developed to accurately predict the physical and chemical properties of fuel.…”

Section: Fuelmentioning

confidence: 99%

“…When constructing surrogate fuels, researchers need to consider physical and chemical properties of real fuels, chemical reaction kinetics processes and other features. ML, has been successfully used to predict fuel properties [30,31] and design the next generation fuels, [32] perhaps will be applied to construct surrogate fuels. Fig.…”

Section: Fuelmentioning

confidence: 99%

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Progress in the Application of Machine Learning in Combustion Studies

Zheng¹,

Lin²,

Yang³

et al. 2020

ES Energy Environ.

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Combustion is the main source of energy and environmental pollution. The objective of the combustion study is to improve combustion efficiency and to reduce pollution emissions. In the past decades, machine learning (ML), as a branch of artificial intelligence, has attracted increasing interests, especially in the combustion field. In the present work, the definition, current status and recent progress in the applications of ML on researches related to combustion are briefly reviewed. Combustion studies combined with ML can be divided into theoretical and industrial aspects. Studies of combustion theory include computational fluid dynamics (CFD) simulation, combustion phenomenon and fuel. ML is used to reduce the cost of CFD, including reducing the scale of combustion mechanism, saving the memory storage of the probability density function table and optimizing Large Eddy Simulation. ML helps in the research of combustion phenomena, such as detecting thermoacoustic combustion oscillation, portioning regimes of ignition and detonation, and reconstructing cellular surface of gaseous detonation. ML has been also applied to study physicochemical properties of fuels and to design the next generation fuels.In the industrial research with respect to combustion, ML is mainly applied to produce electricity and power by power plants or engines, and less to other fields. ML could figure out problems of combustion in various kinds of furnaces and postcombustion emissions in power plants. In addition, ML plays important roles in biodiesel engine, Homogenous Charge Compression Ignition (HCCI), and operation control or monitoring in the engines. Moreover, ML can also be applied to other industrial studies related to combustion, mainly to particulate matters. The methods of the mentioned studies are summarized in details and the potential applications of ML in combustion community are proposed.

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Kinematic viscosity prediction of jet fuels and alternative blending components via comprehensive two‐dimensional gas chromatography, partial least squares, and Yeo‐Johnson transformation

Caceres‐Martinez,

Kilaz

2024

J of Separation Science

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This work presents an accurate yet simplified partial least squares model to predict the kinematic viscosity of conventional and alternative jet fuels at −20°C using comprehensive two‐dimensional gas chromatography coupled to a flame ionization detector (GC × GC/FID). Three different normalization methods (mean‐centering, logarithmic, and Yeo‐Johnson) were evaluated to identify their impact in the prediction of middle distillates’ physical properties. Results using Yeo‐Johnson transformation exhibited improved viscosity prediction capabilities over the validation set with a mean absolute percentage error of 5.3%, a root‐mean‐squared error of 0.23, and a coefficient of determination (R2) of 0.9404 using only 10 latent variables. Unlike previously reported correlations, this model allowed the identification of specific hydrocarbon groups and carbon numbers that drive jet fuel viscosity at low temperatures. The presence of even small amounts of large branched‐alkanes (C15–C17), dicyclic‐alkanes (C10), and cycloaromatics (C11) have the potential to strongly increase the kinematic viscosity of jet fuels. Contrastingly, light monocycloalkanes and branched‐alkanes (≤ C10) were associated with lower viscosity values. Novelly, this model suggests the implementation of Yeo‐Johnson transformations to predict the physical properties of middle distillates to further improve the performance metrics of partial least squares models based on GC data.

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On estimating physical and chemical properties of hydrocarbon fuels using mid-infrared FTIR spectra and regularized linear models

Cited by 41 publications

References 46 publications

Octane Prediction from Infrared Spectroscopic Data

Octane Prediction from Infrared Spectroscopic Data

Progress in the Application of Machine Learning in Combustion Studies

Kinematic viscosity prediction of jet fuels and alternative blending components via comprehensive two‐dimensional gas chromatography, partial least squares, and Yeo‐Johnson transformation

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