A minimalist functional group (MFG) approach for surrogate fuel formulation

Abstract

et al. (2018) A minimalist functional group (MFG) approach for surrogate fuel formulation. Combustion and Flame 192: 250-271. Available: http://dx. AbstractSurrogate fuel formulation has drawn significant interest due to its relevance towards understanding combustion properties of complex fuel mixtures. In this work, we present a novel approach for surrogate fuel formulation by matching target fuel functional groups, while minimizing the number of surrogate species. Five key functional groups; paraffinic CH3, paraffinic CH2, paraffinic CH, naphthenic CH-CH2 and aromatic C-CH groups in addition to structural information provided by the Branching Index (BI) were chosen as matching targets. Surrogates were developed for six FACE (Fuels for Advanced Combustion Engines) gasoline target fuels, namely FACE A, C, F, G, I and J. The five functional groups present in the fuels were qualitatively and quantitatively identified using high resolution 1 H Nuclear Magnetic Resonance (NMR) spectroscopy. A further constraint was imposed in limiting the number of surrogate components to a maximum of two. This simplifies the process of surrogate formulation, facilitates surrogate testing, and significantly reduces the size and time involved in developing chemical kinetic models by reducing the number of thermochemical and kinetic parameters requiring estimation.Fewer species also reduces the computational expenses involved in simulating combustion in practical devices. The proposed surrogate formulation methodology is denoted as the Minimalist Functional Group (MFG) approach. The MFG surrogates were experimentally tested against their target fuels using Ignition Delay Times (IDT) measured in an Ignition Quality Tester (IQT), as specified by the standard ASTM D6890 methodology, and in a Rapid Compression Machine [Type text] 2 (RCM). Threshold Sooting Index (TSI) and Smoke Point (SP) measurements were also performed to determine the sooting propensities of the surrogates and target fuels. The results showed that MFG surrogates were able to reproduce the aforementioned combustion properties of the target FACE gasolines across a wide range of conditions. The present MFG approach supports existing literature demonstrating that key functional groups are responsible for the occurrence of complex combustion properties. The functional group approach offers a method of understanding the combustion properties of complex mixtures in a manner which is independent, yet complementary, to detailed chemical kinetic models. The MFG approach may be readily extended to formulate surrogates for other complex fuels.

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

“…1 H NMR measurements of two naphtha's (light and heavy; LN J80 and HN J80) and one Satorp gasoline (SG) were performed in the present work and the 1 H NMR spectral data of the other real fuels reported here were taken from previous studies [37,69]. Each sample was prepared by dissolving 100 µl of the fuel in 600 µl of deuterated chloroform (CDCl3) and then 600 µl of the mixture was transferred to 5 mm NMR tubes.…”

“…A Bruker 700 MHz AVANACIII NMR spectrometer equipped with Bruker CPTCI multinuclear CryoProbe (BrukerBioSpin, Rheinstetten, Germany) was used for all 1 H NMR measurements. All spectra were collected under the same conditions using same instrumental parameters as described in previous works [37,69]. Bruker Topspin 4.0.4 software was used for data collection and MestReNova was used for spectra post processing and data analyses.…”

“…Fuels can be also be characterized by their relatively small number of molecular moieties, or functional groups, which dictate the combustion characteristics of the fuel like octane number [66], CN [37], flash point [34], sooting tendencies [20], auto-ignition [67], ignition delay [68] etc. Recent works by Abdul Jameel et al [69,70] and Mueller [71,72], have shown that formulating simple surrogates by mimicking the functional groups in the fuel gives the ability to reproduce various physical, thermochemical and combustion properties of the fuel. This approach called as the minimalist functional group (MFG) approach has been comprehensively tested on a number of gasoline, diesel and jet fuels [69,70,73].…”

“…Recent works by Abdul Jameel et al [69,70] and Mueller [71,72], have shown that formulating simple surrogates by mimicking the functional groups in the fuel gives the ability to reproduce various physical, thermochemical and combustion properties of the fuel. This approach called as the minimalist functional group (MFG) approach has been comprehensively tested on a number of gasoline, diesel and jet fuels [69,70,73]. A number of other studies [21,22,64,[74][75][76][77][78] reported in the literature have also hinted at the ability of functional groups to reproduce fuel behavior.…”

Predicting Sooting Propensity of Oxygenated Fuels Using Artificial Neural Networks

“…The input features of the model consist of the fuel functional groups, molecular weight (MW), and a structural parameter called the branching index, which quantifies the branching in a molecule. The combination of these input parameters adequately represents the molecular structure of a pure compound, mixture, or real fuel, and these have been successfully used to predict derived cetane number [47,48], octane number [44], and formulate surrogates for a number of fuels using the minimalist functional group (MFG) approach [49][50][51].…”