# A TRF-iso-Butanol kinetic mechanism for the prediction of pollutant with validation against novel rich atmospheric flame experiments

Timothée Fages, Doha Kdouh, Laurent Gasnot, Luc-Sy Tran, Sylvie Gosselin, Nathalie Lamoureux, René Fournet, Baptiste Sirjean, Pierre-Alexandre Glaude

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## To cite this version:

Timothée Fages, Doha Kdouh, Laurent Gasnot, Luc-Sy Tran, Sylvie Gosselin, et al. A TRF-iso-Butanol kinetic mechanism for the prediction of pollutant with validation against novel rich atmospheric flame experiments. Fuel, 2025, 387, pp.134405. 10.1016/j.fuel.2025.134405. hal-04903729

HAL Id: hal-04903729

https://hal.univ-lorraine.fr/hal-04903729v1

Submitted on 21 Jan 2025

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Distributed under a Creative Commons Attribution 4.0 International License
A TRF-iso-Butanol Kinetic Mechanism for the
Prediction of Pollutant with Validation against Novel
Rich Atmospheric Flame Experiments

Timothée Fages<sup>a*</sup>, Doha Kdouh<sup>b</sup>, Laurent Gasnot<sup>b</sup>, Luc-Sy Tran<sup>b*</sup>, Sylvie Gosselin<sup>b</sup>, Nathalie Lamoureux<sup>b</sup>, René Fournet<sup>a</sup>, Baptiste Sirjean<sup>a</sup>, Pierre-Alexandre Glaude<sup>a</sup>

<sup>a</sup>Université de Lorraine, CNRS, LRGP, F-54000 Nancy, France

<sup>b</sup>Univ. Lille, CNRS, UMR 8522 - PC2A - Physicochimie des Processus de Combustion et de l'Atmosphère, F-59000, Lille, France.

Published in *Fuel* (2025), Volume 387, 134405,

doi.org/10.1016/j.fuel.2025.134405.
Abstract

Light-duty vehicles are increasingly powered by direct-injection gasoline engines, which reduce fuel consumption. However, this type of engine leads to the formation of liquid films of fuel at the wall of the combustion chamber, which then burn in fuel-rich flames, producing nano soot. This issue is a topic of focus in the combustion research community, especially for blends of gasoline with new alcohol biofuels, e.g. iso-butanol. To model these phenomena, a high-temperature combustion model of a toluene reference fuel (TRF) blended with iso-butanol (B) has been developed as a surrogate of a gasoline. The new TRF-B model was built by comparing the performance of recent mechanisms for reproducing a very large number of auto-ignition delay times and experimental speciations for pure fuels and binary and ternary mixtures. The performance and size of mechanisms in the literature led to the final model being built by merging TRF and alkylbenzene mechanisms from LLNL with a sub-mechanism for iso-butanol from MIT. The model was validated by the simulation of the structure of a new premixed laminar flame of the TRF-B blend, which is a surrogate of a RON 95 gasoline, under atmospheric pressure and fuel-rich conditions (Φ=1.72). Reactants, products and numerous intermediates sampled by microprobe were quantified by on-line gas chromatography. Temperature profiles were obtained by thermocouple and NO-LIF thermometry. The main products of the oxidation of each of the reactants are well reproduced by the model, such as propene, iso-butene and benzene, as well as oxygenate compounds characteristic of iso-butanol oxidation such as propanal. The
model reproduces well the formation of unsaturated soot-precursors and will be
extended to the formation of heavier PAHs.

**Keywords:** TRF, iso-butanol, premixed laminar flame, gasoline, kinetic modeling

*Corresponding author.
# 1. Introduction

Despite the expected increase in the share of electric vehicles in the coming years [1], internal combustion engines will remain in the majority in 2030 with a growing share for hybrid vehicles. At the same time, the rapid decline of diesel engines in light vehicles, accentuated by recent issues concerning their soot emissions, greatly favors gasoline engines [2]. Among these engines, advances in electronic injection control are strongly promoting direct injection [3], with a better control of the equivalence ratio and injection timing, reducing theoretically fuel consumption by 20-25% [4,5]. Nevertheless, these engines suffer from soot emissions, in particular the more health-endangering nano-soot, which will be regulated in future emission standards. Nanoparticles of soot do not come from the main combustion, but rather from pool fires above liquid fuel films adsorbed on the chamber walls [6]. In these flames, significant temperature and equivalence ratio gradients promote soot formation in rich zones [6].

At the same time, the reduction of our reliance on fossil fuels and of carbon emissions in transportation, promotes the use of renewable biofuels, which global amount should increase by around 50% by 2030 [1]. As they are composed of oxygenated molecules, often alcohols, future fuels will contain a higher mass of oxygen than today (around 10%), changing their heating value, as well as combustion products. Despite a reduction in soot emissions [7], oxygenated fuels raise new problems such as an increase in the production of nitrogen oxides (NOx) or in aldehydes, which are harmful for human health.

Ethanol, currently used in fuel blends, is one of the alcohols with the lowest energy density [8]. Moreover, ethanol production mainly uses food resources such as corn, wheat, beet, and sugar cane. Depending on how it is produced, ethanol's lifecycle carbon footprint can range from very
advantageous to equivalent or even worse than gasoline [9]. As a result, the search for new biofuels is moving towards heavier alcohols, with a higher C/O ratio. However, the need to produce these molecules industrially and ecologically greatly limits the number of alcohols that can be used. Iso-butanol is one of the possible options [10]. The Low Heating Value (LHV) of butanol is 29 MJ/l, which is close to that of gasoline SP95 (32 MJ/l) and above that of ethanol (23 MJ/l) [11]. Among the four butanol isomers, iso-butanol has the highest RON with a value of 113 and some industrialization processes are already underway to produce it [12–14]. Olson et al. [15] reviewed the performance of engine and vehicle using neat iso-butanol or blended with gasoline or diesel. They showed that, in direct-injection spark-ignition engine, iso-butanol blended at 25 vol% in gasoline halved particulate number emissions, compared with pure gasoline. Iso-butanol blended gasoline also exhibits improved knock resistance and reduced NOₓ and CO emissions; however, it leads to an increase in aldehyde emissions and a reduction in power output. It should be noted that the performance of this blend is highly dependent on engine technology and operating conditions.

As a result, we are likely to see blends of gasoline and iso-butanol in the near future. Emissions of these blends need to be predicted quantitatively thanks to kinetic models based on a surrogate of a gasoline fuel such as TRF (Toluene Reference Fuels, composed of iso-octane, n-heptane and toluene) with iso-butanol (TRF-B). Few TRF-B mechanisms already exist [16–20], but are mainly validated on low temperature conditions (rapid compression machine) at the equivalence or in lean mixtures. Yet, none are validated for high temperature in rich conditions nor against a large bench of experimental data. The present study aims to develop a detailed high-temperature mechanism of TRF-B based on sub-mechanisms already available for TRF and alcohols, and valid for the simulation of fuel rich conditions. Validation of this mechanism was made against
numerous high-temperature experimental data from the literature [21–39]. However, experimental data for rich conditions are scarce, even more so when it comes to specific mixtures. To validate the newly-developed model and explore the combustion chemistry of TRF-iso-butanol blend under these conditions, new species profiles of an atmospheric laminar fuel-rich TRF-B flame burner were obtained in this study. To our knowledge, no such detailed mechanism validated against speciation data is currently available in the literature.

## 2. Experiment

**Figure 1** presents a schematic diagram of the used experimental setup. It includes a flame burner, three Gas Chromatography (GC) setups and temperature analysis setups. These setups are described below.

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Experimental setup including two liquid fuel systems, a burner, and analytical techniques.</figcaption></figure>
## 2.1. Atmospheric flame burner setup

The burner is a McKenna atmospheric flat laminar premixed flame burner, which is similar to that described previously in Do et al. and Sood et al. [40,41]. It consists of a central bronze porous plug (60 mm in diameter). The porous zone is surrounded by another co-annular porous used for the nitrogen shroud (27 Lₙ/min; Lₙ is liter at 0 °C and 1 atm). The temperature of the burner is constantly maintained at 55°C using a cryostat. A stagnation plate (60 mm in diameter and 30 mm thick) was positioned 21 mm above the burner surface in order to stabilize fuel-rich flames. The temperature of the plate measured for the present flame is 829 K. The plate has a central opening for the sampling quartz microprobe and an additional metallic grid was placed directly on the stagnation plate, to avoid peripheral flame ignition.

In this study, liquid TRF fuel was prepared by mixing the 3 pure compounds (purity >99%) and verified using GC analysis, and subsequently used in System 1 (Fig. 1). System 2 (Fig. 1) was filled with iso-butanol (purity >99%). The liquid mass flow rate of the TRF fuel was controlled using a Coriolis flow controller (Bronkhorst, error ±0.2%) followed by an evaporator/mixer, in which TRF-B is evaporated and mixed with N₂. The gaseous stream is then mixed with O₂ and CH₄ and fed to the burner. CH₄ was necessarily used to stabilize the flame. Flow rates of CH₄ (Air Liquide, purity ≥99.95%), O₂ and N₂ (Air Liquide, purity ≥99.995%) were controlled using mass flow controllers (MFC, Bronkhorst, error ±0.5%).

Flame temperature profiles as a function of the height above the burner (HAB) were measured, with and without sampling probe, using a combination of thermocouple method and NO-LIF (Laser Induced Fluorescence) thermometry [42]. The former technique was recently reported in Elias et al. and Sood et al. [43,44] wherein a Type S thermocouple, characterized by a wire
diameter of 0.2 mm, was employed with radiation loss correction based on extrapolation to zero
wire diameter. The thermocouple was inserted into the middle of the flame using a remote-
controlled system, making the process automatic and consistent. The second technique, NO-LIF
thermometry, was used with an objective to doublecheck the high temperature zone where
radiation loss is significant. Here, a small amount of NO (500 ppm in order to not perturb the
chemical structure) was substituted to the nitrogen diluent to provide a good signal-to-noise ratio.
The laser wavelength was tuned over 225.65 – 225.73 nm to excite NO in the A-X(0,0) band, the
LIF signal, in the linear regime, was collected at 245 nm. Temperatures were determined by
fitting synthetic LIF excitation spectra to the experimental spectra following a procedure
previously detailed [42]. The two methods of temperature measurement present a good
agreement as presented in Section 5. The temperature uncertainty is around 100 K at the
maximum of the temperature profile.

## 2.2. Chemical species measurements

A quartz sampling microprobe (orifice ~180 µm) was used to collect the gaseous samples from
the studied flame at different HABs in order to be able to identify and quantify the chemical
species produced during combustion processes by using three GC systems. Gaseous samples are
directed to GCs through a transfer line that was heated at 140 °C to avoid condensation of the
heavy products. The first GC (GC1) system is a CP-3800 Varian equipped with two columns
(CP-Al₂O₃/KCl and Molecular Sieve 5A) and two detectors, a flame ionization detector (FID)
and a thermal conductivity detector (TCD). This system was used to measure CO and
hydrocarbon species. The second GC (GC2) is a Perkin Elmer Clarus 580 equipped with two
columns (Rt-QBond and Molecular Sieve 5A), a TCD and an FID with methanizer (nickel
catalyst hydrogenating reactor). This system was used to analyze mainly O₂, N₂ and oxygenated
species (alcohols, aldehydes, and ketones). Several species were detected by both GCs, which allows comparing the results and thereby reinforcing the confidence in our measurements. The third GC system (Agilent Technologies 5975C), which is equipped with MS with electron ionization at 70 eV, is used for the identification of chemical species by comparison of the obtained MS spectra with the NIST database. Chemical species were identified by their individual retention times and MS spectra. The direct calibration has been done using calibrated commercial mixtures, when possible, otherwise calibration factors were estimated based on effective carbon number approach (GC1) or the hydrogenation by the methanizer converting species to the corresponding alkane (GC2). The uncertainties are <15% for main species (H₂, O₂, N₂, CO, CH₄, n-heptane, toluene, iso-butanol, isooctane), and <25% for abundant intermediates (every other species in this study), which were estimated based on the error of the MFCs used, the error of the composition of commercially available standard mixtures, the calibration methods, and the signal-to-noise ratio.

### 3. TRF kinetic mechanism: selection and modifications

Before adding the iso-butanol sub-mechanism, the development of an accurate TRF mechanism is crucial. This is the objective of this section. Nine models from the literature and three modified models have been compared over a wide range of experimental data in order to select the most suitable model. NUIGMech1.2 [45], EXGAS [46] generated model or LLNL Gasoline Surrogate mechanism [47] are a few examples. The modified models are based either on LLNL or NUIGMech1.2, for which we added a sub-mechanism, reduced the mechanism, or complemented a sub-mechanism. While the full analysis and selection process between these twelve mechanisms can be found in the SM1, this section will only focus on the development of the selected mechanism, namely ALLNLV2. This mechanism was developed in this study and
has the LLNL Gasoline Surrogate [47] mechanism as its main source. The selection was made
taking into account the accuracy of the models, their size and their ability to converge on
ANSYS Chemkin-Pro [48] or Cantera [49], especially for premixed laminar flames.

3.1. *Modification of LLNL*

***Improving aromatic sub mechanism of LLNL***

The poor performances of the LLNL model of toluene combustion on some experimental
conditions (see SM1) and the existence of more recent studies from this team led us to update the
reactions of alkyl aromatics by merging LLNL with the AlkylAromaticV2.1 mechanism (named
AA) [50]. This latter was developed specifically for alkyl aromatics. However, merging two
mechanisms manually is very time-consuming and error prone. If there is a high degree of
similarity in species names between the two mechanisms or if the sub-mechanism to be extracted
has no coupling species or reactions with the rest of the mechanism, merging can be easily
achieved. Unfortunately, there are not enough similarities in the case of the LLNL and AA
models. An in-house code, named TIRAMISU (Thermodynamical Interpretation of Reactions
and Automated Merging for Inter-Scheme Union) [51], has been developed to assist the user in
merging two detailed mechanisms. TIRAMISU solves the differences in species names by
translating most of the species of the two mechanisms thanks to their thermochemical data.
Eventually, species which have not been automatically translated have to be processed manually.
Thus, TIRAMISU either merges the two mechanisms completely or extracts a sub-mechanism
from one of the models and merges it with the other. In the case of the fusion between LLNL and
AA, species higher than ethylbenzene were not added to LLNL. The new model created was
named ALLNLV2 and its size was increased from 1389 species and 5935 reactions to 1449 species and 6570 reactions.

## 3.2. Literature's experimental data used for the comparison between models

Different types of experimental data were used to assess the accuracy of the models. These data concern the pure constituents of the TRF mixture, as well as binary mixtures and the TRF itself. As the aim of the model is to simulate the formation of pollutants in flames on the engine walls, only high-temperature conditions (T>1000K) have been used.

### Ignition delay times

First, ignition delay times (IDT), which are representative of the global reactivity of gasoline in combustion, have been simulated. High temperatures (1000 - 2000 K) and various pressures and equivalence ratios have been considered to select experimental data from the literature (Table 1). All these data correspond to shock tube (ST) experiments.

<table><caption>**Table 1.** Experimental conditions of the IDT data available in the literature for H=n-heptane, O=iso-octane, T=toluene and their blends HO, HT, OT and TRF. 821 data points were simulated including 551 above 1000 K.</caption><thead><tr><td>Fuels</td><td>T (K)</td><td>P (atm)</td><td>Φ</td><td>Ref.</td></tr></thead><tbody><tr><td>O</td><td>1177-2009</td><td>1.10-12.58</td><td>0.25-2</td><td>[21]</td></tr><tr><td>H TRF</td><td>850-1280</td><td>15-60</td><td>0.5-2</td><td>[22]</td></tr><tr><td>O T</td><td>855-1269</td><td>14-59</td><td>0.5-1</td><td>[23]</td></tr><tr><td>HT</td><td>620-1180</td><td>10-50</td><td>0.3-1</td><td>[24]</td></tr></tbody></table>
<table><tr><td>HO HO<br>HT OT TRF</td><td>1200-1600</td><td>2.5</td><td>1</td><td>[25]</td></tr><tr><td>T</td><td>1021-1400</td><td>10-61</td><td>0.25-1</td><td>[26]</td></tr><tr><td>HO T HT OT</td><td>700-1200</td><td>40</td><td>0.5-1</td><td>[27]</td></tr><tr><td>H</td><td>1200-1500</td><td>2-10</td><td>0.5-1</td><td>[28]</td></tr><tr><td>HO</td><td>700-1200</td><td>10-40</td><td>0.5-1</td><td>[29]</td></tr></table>

## Species profiles

The models must be able to reproduce the main products experimentally detected with a good accuracy. Species profiles can be obtained in many ways, such as in laminar flame (in a flat flame burner, FFB) as a function of distance above the burner or in ST as a function of operating conditions such as temperature or residence time. The experimental conditions considered in this work are summarized in **Table 2**.
<table><caption>**Table 2.** Experimental conditions of the species profiles data available in the literature for H=n-</caption><thead><tr><th>Fuel</th><th>T (K)</th><th>P (atm)</th><th>Φ</th><th>τ (ms)</th><th>Ref.</th></tr></thead><tbody><tr><td>T</td><td>400-2100</td><td>0.04</td><td>1.75</td><td>-</td><td>[30]</td></tr><tr><td>H O</td><td>550-1650</td><td>1</td><td>1.9</td><td>-</td><td>[31]</td></tr><tr><td>T-Py</td><td>1000-1950</td><td>27-45</td><td>-</td><td>1.5</td><td>[32,33]</td></tr><tr><td>O O-Py</td><td>800-1800</td><td>25-55</td><td>0.52-1.67</td><td>1.8-2.3</td><td>[34]</td></tr><tr><td>H-Py</td><td>1000-1500</td><td>1.85-1.9</td><td>-</td><td>1.7-1.8</td><td>[35]</td></tr><tr><td>T-Py</td><td>1000-1850</td><td>0.0065-1</td><td>-</td><td>-</td><td>[36,37]</td></tr></tbody></table>

heptane, O=iso-octane, T=toluene and with -Py=pyrolysis conditions.

### 3.3. *Comparison between the models*

#### Accuracy on IDT

All IDT were calculated with ANSYS Chemkin-Pro [48] in an adiabatic homogeneous closed reactor (0D). Model accuracy was estimated from the Error Factor (EF) according to **equation (1)** on all experimental data for a pure component or a mixture. It is then averaged for each blend. The advantage of the EF is that it is easy to interpret. For example, an EF of 5 means that the IDT would differ from the experimental data by a factor of 5, whether it is 20% or 500% of the experimental value. This value does not give any information about the over- or under-estimation of a mechanism, but allows to assess the average absolute deviation of a set of points. However, the EF does not favor low values in the same way as the absolute relative error or the root mean square error.
$$EF = \max \left( \frac{X_{\text{simulation}}}{X_{\text{experimental}}}, \frac{X_{\text{experimental}}}{X_{\text{simulation}}} \right) \quad (1)$$

The results are summarized in **Table 3**, which collects the EF score on each mixture and the average score of LLNL and ALLNLV2.

**Table 3.** EF of simulated high-temperature IDT, with T=toluene, H=n-heptane, O=iso-octane and their blend. The two last rows represent the average accuracy for T>1000K and T>1100K. Red and green represent which model is less or more accurate (respectively), per row.

<table><thead><tr><th>Fuels</th><th>LLNL</th><th>ALLNLV2</th></tr></thead><tbody><tr><td>T</td><td>1.59</td><td>1.43</td></tr><tr><td>H</td><td>1.57</td><td>1.60</td></tr><tr><td>O</td><td>1.68</td><td>1.62</td></tr><tr><td>HT</td><td>1.35</td><td>1.32</td></tr><tr><td>OH</td><td>1.44</td><td>1.46</td></tr><tr><td>OT</td><td>1.96</td><td>1.74</td></tr><tr><td>TRF</td><td>1.38</td><td>1.33</td></tr><tr><td>Average T>1000K</td><td>1.57</td><td>1.50</td></tr><tr><td>Average T>1100K</td><td>1.66</td><td>1.58</td></tr></tbody></table>

Overall, the new ALLNLV2 mechanism is more accurate than the LLNL model, which was expected. A deterioration is observed only for *n*-heptane and iso-octane/*n*-heptane blend, however, by only 0.02 or 0.03. All values are within the order of magnitude of most experimental uncertainties for IDT. For example, in J. Herzler et al. [24], an uncertainty of 25 K on their data is equivalent to an average uncertainty of 1.5.

Two parity plots presenting the performance of the two models versus experimental data and the deviation of ALLNLV2 versus LLNL are presented in **Figure 2. Figure 2 (a)** shows only a little difference between the two mechanisms. However, ALLNLV2 seems to be a little more reactive
than LLNL. This trend is confirmed thanks to **Figure 2 (b)**, with the deviation of ALLNLV2 to LLNL. Almost all ALLNLV2 IDT are shorter than that calculated with LLNL, which mean that the first mechanism as a higher reactivity on every fuel and blends. Some IDT against temperature plot can be found in supplementary material.

(a)

(b)
<figure><img src="image_4.png" /><figcaption>**Figure 2.** (a) Parity plot of the experimental versus simulated IDT for ALLNLV2 and LLNL. (b)</figcaption></figure>

Parity plot of ALLNLV2 IDT deviation to LLNL IDT. Open (empty) symbols represent IDT where the starting temperature is between 1000 and 1100 K while closed (filled) symbols represent IDT with a starting temperature above 1100 K.

Accuracy on flame species profiles

Species profiles were calculated with ANSYS Chemkin-Pro software and for a Premixed Burner-
stabilized Flame reactor model (1D) using the measured temperature profiles in the
corresponding experiments. Simulations were compared on the basis of the maximum mole
fraction at the peak of each species studied, using the EF relative to this maximum. This method
therefore only compares intermediate species. The profiles of the reactants and products are
evaluated qualitatively separately. The reason for focusing solely on the maxima of the species
profiles, rather than the overall differences, is that the position of the peak of profiles is highly
dependent on the experimental temperature profile, but the latter always has a high degree of
uncertainty. The probe used for species sampling causes significant disturbance to the flame as it
**Table 4.** EF of simulated flame species profiles and the number of species reaching the ceiling. H=n-heptane, O=iso-octane, T=toluene. Red and green highlight which model is less or more accurate (respectively), per row.

approaches the burner. The temperature profile is not the same depending on whether the probe
is close to the burner, far from the burner or absent. The simulation therefore does not take
proper account of this phenomenon due to the use of a single temperature profile, which results
in an offset of the profiles.

The average of the deviations of the flame species is then calculated by adding an EF=10 ceiling,
for species missing from the mechanism (EF is therefore infinite) or too poorly represented
(EF>10). The EF are reported in **Table 4** with the number of species reaching the ceiling for
LLNL and ALLNLV2. All the profiles of species above the ceiling can be seen in **Figure 3**.
<table><thead><tr><th colspan="2">Ref. / Fuels</th><th>LLNL</th><th>ALLNLV2</th></tr></thead><tbody><tr><td rowspan="2">Li et al, 2018 [30]</td><td>T<sup>a</sup></td><td>EF<br/>Nb EF&gt;10</td><td>4.91<br/>5</td><td>2.59<br/>1</td></tr><tr><td>H<sup>b</sup></td><td>EF<br/>Nb EF&gt;10</td><td>1.80<br/>0</td><td>1.75<br/>0</td></tr><tr><td rowspan="2">El Bakali et al, 1998 [31]</td><td>O<sup>c</sup></td><td>EF<br/>Nb EF&gt;10</td><td>2.36<br/>0</td><td>2.65<br/>0</td></tr><tr><td>Average</td><td>EF</td><td>3.02</td><td>2.34</td></tr></tbody></table>

Species used in descending mole fraction in:

ALLNLV2 shows better results for toluene and a more comprehensive chemistry. The missing species (EF&gt;10) in LLNL are almost all present in ALLNLV2. The only species reaching the ceiling for ALLNLV2 is indenyl (C<sub>9</sub>H<sub>7</sub>) with an EF of 10.84, close to the ceiling value, and is the only completely missing species in LLNL (EF=∞). Also, although indene (C<sub>9</sub>H<sub>8</sub>) is included in LLNL, only one reaction is present (C<sub>5</sub>H<sub>5</sub>+C<sub>5</sub>H<sub>6</sub>=C<sub>9</sub>H<sub>8</sub>+CH<sub>3</sub>). This appears insufficient since the EF of C<sub>9</sub>H<sub>8</sub> is over 170 while 3.09 in ALLNLV2 with 24 more reactions. This improvement for the toluene chemistry justifies the addition of the aromatic sub-mechanism in ALLNLV2.

<footnote><sup>a</sup>T: C<sub>2</sub>H<sub>2</sub>, C<sub>6</sub>H<sub>6</sub>, C<sub>4</sub>H<sub>2</sub> (diacetylene) C<sub>2</sub>H<sub>4</sub>, CH<sub>3</sub>, CH<sub>2</sub>O, C<sub>3</sub>H<sub>3</sub> (propargyl), C<sub>6</sub>H<sub>5</sub>C<sub>2</sub>H<sub>5</sub>, p-C<sub>3</sub>H<sub>4</sub> (propyne), C<sub>6</sub>H<sub>5</sub>C<sub>2</sub>H, C<sub>6</sub>H<sub>5</sub>C<sub>2</sub>H<sub>3</sub>, C<sub>4</sub>H<sub>6</sub>, C<sub>10</sub>H<sub>8</sub> (naphthalene), a-C<sub>3</sub>H<sub>4</sub> (allene), C<sub>9</sub>H<sub>8</sub> (indene), C<sub>9</sub>H<sub>7</sub> (indenyl).</footnote>

<footnote><sup>b</sup>H: C<sub>2</sub>H<sub>4</sub>, C<sub>2</sub>H<sub>2</sub>, CH<sub>4</sub>, C<sub>3</sub>H<sub>6</sub>, C<sub>2</sub>H<sub>6</sub>, 1-C<sub>4</sub>H<sub>8</sub> (1-butene), 1,3-C<sub>4</sub>H<sub>6</sub> (1,3-butadiene), 1-C<sub>5</sub>H<sub>10</sub> (1-pentene), p-C<sub>3</sub>H<sub>4</sub>, C<sub>3</sub>H<sub>8</sub>, 1-C<sub>6</sub>H<sub>12</sub> (1-hexene), C<sub>4</sub>H<sub>2</sub>, a-C<sub>3</sub>H<sub>4</sub>, C<sub>4</sub>H<sub>4</sub> (vinylacetylene), C<sub>6</sub>H<sub>6</sub>.</footnote>

<footnote><sup>c</sup>O: CH<sub>4</sub>, C<sub>2</sub>H<sub>2</sub>, C<sub>2</sub>H<sub>4</sub>, i-C<sub>4</sub>H<sub>8</sub> (isobutene), C<sub>3</sub>H<sub>6</sub>, C<sub>2</sub>H<sub>6</sub>, p-C<sub>3</sub>H<sub>4</sub>, a-C<sub>3</sub>H<sub>4</sub>, 1-C<sub>4</sub>H<sub>8</sub>, 1,3-C<sub>4</sub>H<sub>6</sub>, C<sub>6</sub>H<sub>6</sub>, C<sub>4</sub>H<sub>4</sub>, C<sub>4</sub>H<sub>2</sub>, C<sub>3</sub>H<sub>8</sub>.</footnote>
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Species profile of LLNL species with EF>10. Highlight represents the experimental uncertainty. Experimental data are taken from [30].</figcaption></figure>

For the flame of n-heptane, results are almost equivalent with both mechanisms. However, for the flame of isooctane, slightly degraded results with ALLNLV2 compared to LLNL (-0.3) are mainly due to an under-estimation of benzene. Another difference between the two mechanisms is the behavior of allene and propyne, which seem to be reversed for the flames of isooctane and n-heptane. When LLNL produces more allene than ALLNLV2, ALLNLV2 produces more propyne than LLNL. Overall, ALLNLV2 shows better results. The profiles of the reactants and main products of these three flames can be found in the supplementary material.
## Accuracy on plug flow and shock tube species profiles

The simulations were carried out with ANSYS Chemkin-Pro using the homogeneous closed adiabatic reactor (0D) or plug flow reactor code, depending on the experiment performed. For these simulations, a more sophisticated measure of the deviation is used because each data point is derived from an independent experiment, unlike the flame profiles. This deviation is derived from the Curve Matching error (CM) initially presented by Bernardi et al. and taken up by others [52–54]. To measure the CM, the data (experimental and simulated) is considered as a noisy point evaluation of an underlying smooth function. This function will be approximate using a more or less smoothed B-spline [55] (mostly cubic) for each data set. The experimental curves, F, are then compared with the simulated curves (on their common definition domain D, and once normalized), G, using several measurements of the deviation between the curves.

Compared to Girhe et al. [54], the deviation $d_{L2}^0$ has been replaced by $d_{EF}^0$ (equation (2)) in order to use the EF instead of the mean squared error, to not favor low values as for the accuracy of the IDT above. However, the derivative $d_{L2}^1$ (equation (3)) remain the same since, unlike the mole fraction of species, the derivative can be negative and the EF is meaningless if the values are of opposite sign.

$$d_{EF}^0 = \frac{1}{D} \int_D \frac{1}{EF} dx \quad \text{or} \quad \frac{1}{D} \int_D \min\left(\frac{F(x)}{G(x)}, \frac{F(x)}{G(x)}\right) dx \qquad (2)$$

$$d_{L2}^1(F', G') = \frac{1}{1 + \frac{\sqrt{\int_D (F'(x) - G'(x))^2 dx}}{D}} \qquad (3)$$
As with the flame profiles, the maximums of the F and G curves will also be considered in the CM calculation, through the inverse of $EF_{max}$ (to constrain the value between 0 and 1), in order to increase the importance of having the right maximum mole fraction (although this is already calculated in $d_{EF}^0$).

$d_{Pe}^0$ and $d_{Pe}^1$ (equations (4)(5)) are related to the Pearson correlation coefficient ($\rho$) between F and G ($d_{Pe}^0$) and their derivative F' and G' ($d_{Pe}^1$).

$$d_{Pe}^{0}(F, G) = 1 - \frac{1}{2}(1 - \rho(F, G)) \qquad (4)$$

$$d_{Pe}^{1}(F', G') = 1 - \frac{1}{2}(1 - \rho(F', G')) \qquad (5)$$

S (equation (6)) is the optimal horizontal shift between F and G that leads to the maximum values of $d_{EF}^0$, $d_{L^2}^1$, $d_{Pe}^0$ and $d_{Pe}^1$. Those optimized values will be used to calculate the CM (equation (7)).

$$S = 1 - \frac{\operatorname{argmax}(d_{EF}^{0} + d_{L^2}^{1} + d_{Pe}^{0} + d_{Pe}^{1})}{D} \qquad (6)$$

$$CM = \frac{EF_{max}^{-1} + d_{EF}^{0} + d_{L^2}^{1} + d_{Pe}^{0} + d_{Pe}^{1} + 2S}{7} \qquad (7)$$

During the calculation of S, only the G curve is shifted and the studied interval remains the same than the F curve (once normalized, it is [0,1]). If the shifted G does not cover the entire [0,1] domain, G is extrapolated. In addition, as some points have been retrieved from figures, low values (<5% of the maximum) can easily have an order of magnitude of uncertainties, which can generate high deviations. The range of definition of the experimental curve (F) is therefore reduced to values greater than 5% of the maximum mole fraction of the curve.

In the case of a species profile where $EF_{max}^{-1}$ is less than 0.001, the species is considered absent from the mechanism and each of the CM components is set to 0.
**Figure 4** shows some example with, on the left, the species profiles of isooctane, acetylene and CO₂ from the same experiment (isooctane in shock tube at 24 atm and 1.0 Φ [34]). On the right, their approximate curve, shifted for the LLNL and ALLNLV2 mechanisms. For isooctane consumption, the mechanisms agree with experimental data and almost no shift was applied. For the production of CO₂ and C₂H₂, the mechanisms are slightly delayed, and must be shifted by about -40 K. Also, the shape of C₂H₂ of LLNL is less in agreement with the experiment. The same type of reactant profile for all plug flow and shock tube experiment can be found in the

<figure><img src="image_6.png" /></figure>

**Figure 4.** Species profile and their approximate curves F (experience) and G (mechanisms, shifted). Highlight represents the experimental uncertainty. Experimental data are taken from [34].
supplementary material.

**Table 5** presents the CM and its component obtained from the simulation of different experimental conditions. As it can be seen, ALLNLV2 is globally more accurate. It has a better accuracy on almost all parameters. It should be noted that LLNL does not reach the floor value of $EF_{max}^{-1}>0.001$ for every species, which degrade significantly its accuracy. For example, in condition (2), LLNL does not reach this value for 2-butyne in oxidation and pyrolysis and also for phenylacetylene in pyrolysis. The mechanism also does not reach this value for allene in condition (3). In ALLNLV2, every species reach this floor. Even when there is no lacking species, ALLNLV2 seems to have a better $EF_{max}^{-1}$ and be less shifted (higher S) than LLNL.

In conclusion, the ALLNLV2 mechanism is more accurate on almost all comparisons with experimental data. This mechanism was retained to be completed with an iso-butanol sub-mechanism.

**Table 5.** CM error and its component for simulated species profiles in of shock tubes (ST) and plug flow reactor (PFR). The colors in the last column represent which model is more or less accurate, per parameter. The colors of the other columns represent a performance gradient between the worst deviation (here 0.32) and the best (1.0). H=n-heptane, O=iso-octane, T=toluene, P=pyrolyse, (1) = [32,33], (2) = [34], (3) = [36,37], (4) = [35].
<table><thead><tr><th>Parameters</th><th>EF<sub>max</sub><sup>-1</sup></th><th>d<sub>EF</sub><sup>0</sup></th><th>d<sub>L2</sub><sup>1</sup></th><th>d<sub>Pe</sub><sup>0</sup></th><th>d<sub>Pe</sub><sup>1</sup></th><th>S</th><th>CM</th></tr></thead><tbody><tr><td colspan="8" style="text-align: center; font-weight: bold;">LLNL</td></tr><tr><td>(1) T P 27 atm</td><td>0.41</td><td>0.32</td><td>0.73</td><td>0.95</td><td>0.80</td><td>0.59</td><td style="color: red;">0.63</td></tr><tr><td>(1) T P 45 atm</td><td>0.53</td><td>0.40</td><td>0.71</td><td>0.95</td><td>0.89</td><td>0.53</td><td style="color: red;">0.65</td></tr><tr><td>(2) O 0.5 Φ 55 atm</td><td>0.61</td><td>0.56</td><td>0.67</td><td>0.94</td><td>0.90</td><td>0.88</td><td style="color: red;">0.78</td></tr><tr><td>(2) O 1.0 Φ 24 atm</td><td>0.59</td><td>0.57</td><td>0.69</td><td>0.92</td><td>0.89</td><td>0.85</td><td style="color: red;">0.77</td></tr><tr><td>(2) O 1.7 Φ 51 atm</td><td>0.57</td><td>0.55</td><td>0.66</td><td>0.93</td><td>0.90</td><td>0.87</td><td style="color: red;">0.77</td></tr><tr><td>(2) O P 26 atm</td><td>0.51</td><td>0.45</td><td>0.62</td><td>0.87</td><td>0.84</td><td>0.78</td><td style="color: red;">0.70</td></tr><tr><td>(2) O P 55 atm</td><td>0.51</td><td>0.45</td><td>0.59</td><td>0.88</td><td>0.84</td><td>0.78</td><td style="color: red;">0.69</td></tr><tr><td>(3) T P 1 atm</td><td>0.46</td><td>0.41</td><td>0.66</td><td>0.89</td><td>0.84</td><td>0.73</td><td style="color: red;">0.67</td></tr><tr><td>(4) H P 2 atm</td><td>0.87</td><td>0.82</td><td>0.82</td><td>1.00</td><td>0.81</td><td>0.82</td><td style="color: red;">0.85</td></tr><tr><td style="font-weight: bold;">Average</td><td>0.56</td><td>0.50</td><td>0.68</td><td>0.93</td><td>0.86</td><td>0.76</td><td style="color: red;">0.72</td></tr><tr><td colspan="8" style="text-align: center; font-weight: bold;">ALLNLV2</td></tr><tr><td>(1) T P 27 atm</td><td>0.52</td><td>0.36</td><td>0.74</td><td>0.95</td><td>0.79</td><td>0.70</td><td style="color: green;">0.68</td></tr><tr><td>(1) T P 45 atm</td><td>0.65</td><td>0.48</td><td>0.72</td><td>0.97</td><td>0.90</td><td>0.70</td><td style="color: green;">0.73</td></tr><tr><td>(2) O 0.5 Φ 55 atm</td><td>0.64</td><td>0.56</td><td>0.70</td><td>0.99</td><td>0.95</td><td>0.94</td><td style="color: green;">0.82</td></tr><tr><td>(2) O 1.0 Φ 24 atm</td><td>0.63</td><td>0.58</td><td>0.74</td><td>0.99</td><td>0.96</td><td>0.92</td><td style="color: green;">0.82</td></tr><tr><td>(2) O 1.7 Φ 51 atm</td><td>0.62</td><td>0.56</td><td>0.71</td><td>0.99</td><td>0.95</td><td>0.94</td><td style="color: green;">0.82</td></tr><tr><td>(2) O P 26 atm</td><td>0.62</td><td>0.53</td><td>0.73</td><td>0.97</td><td>0.93</td><td>0.91</td><td style="color: green;">0.80</td></tr><tr><td>(2) O P 55 atm</td><td>0.63</td><td>0.53</td><td>0.72</td><td>0.98</td><td>0.94</td><td>0.90</td><td style="color: green;">0.80</td></tr><tr><td>(3) T P 1 atm</td><td>0.60</td><td>0.65</td><td>0.72</td><td>0.98</td><td>0.90</td><td>0.81</td><td style="color: green;">0.78</td></tr><tr><td>(4) H P 2 atm</td><td>0.90</td><td>0.87</td><td>0.83</td><td>1.00</td><td>0.80</td><td>0.88</td><td style="color: green;">0.88</td></tr><tr><td style="font-weight: bold;">Average</td><td>0.65</td><td>0.57</td><td>0.74</td><td>0.98</td><td>0.90</td><td>0.86</td><td style="color: green;">0.79</td></tr></tbody></table>
## 4. TRF-B kinetic mechanism

Starting from the ALLNLV2 mechanism, three mechanisms including iso-butanol sub-mechanisms from the literature (Moss-2008 [38], Sarathy-2012 [56] and Merchant-2013 [57], respectively) were generated with TIRAMISU and used to simulate auto-ignition times and species profiles.

### 4.1. Experimental IDT used for the comparison between models

The three generated mechanisms were tested only on iso-butanol IDT as we do not change the core-reactions of ALLNLV2 but only add a sub-mechanism. The operating conditions relative to IDT are presented in Table 6.

<table><caption>**Table 6.** Experimental condition of iso-butanol IDT.</caption><thead><tr><td>T (K)</td><td>P (atm)</td><td>Φ</td><td>Number of IDT</td><td>Ref.</td></tr></thead><tbody><tr><td>1196-1654</td><td>1.2-4.3</td><td>0.25-1</td><td>36</td><td>Moss et al. 2008 [38]</td></tr><tr><td>945-1763</td><td>1.2-11.1</td><td>0.5-2</td><td>127</td><td>Pan et al. 2014 [39]</td></tr></tbody></table>

### 4.2. Accuracy on IDT

The EF estimation for the three iso-butanol models (Moss-2008, Sarathy-2012 and Merchant-2013) and the three merged models (V2+Moss, V2+Sarathy, V2+Merchant with V2+=ALLNLV2) are reported in **Figure 5**.

It is necessary to carry out the merger before drawing any conclusions based on the performance of the iso-butanol models. After the merging step, the extracted sub-mechanism could be less
<figure><img src="image_7.png" /></figure>

accurate than in its original model. Sarathy-2012 and Merchant-2013 are examples of this
behavior, due probably because of different rate constants or thermodynamic data in their core
mechanism. Overall, the Merchant-2013 iso-butanol sub-mechanism is the most accurate after
merging. However, its large size is a concern. Whereas the models of Sarathy et al. and Moss et
al. contain about 20 species and 250 reactions, the iso-butanol sub-mechanism of Merchant et al.
involves about 70 species and 1,000 reactions after extraction by the TIRAMISU software [51].
Nevertheless, the gain in precision between the three models let the ALLNLV2-Merchant (called
ALLNLV2_B) mechanism unchallenged and the best compromise for testing the model for
species profiles in a laminar flame burner. A more in-depth analysis of these three merged model
can be found in Fages et al. [51].
**Figure 5.** EF of original and merged mechanisms (V2+ = ALLNLV2). Adapted from Fages et al. [51].

(a)

(b)

To improve simulation time and avoid convergence issues for time-consuming calculation such as flame or CFD, some adjustments have been made. The ALLNLV2_B model has been reduced by removing all low-temperature reactions as they do not play any noticeable role in flame conditions [58]. First, all species containing simultaneously more than one oxygen atom and more than two carbon atoms, and their reactions, have been removed. After this step, some verifications have been made to remove species which, as a result of the reduction, are no longer produced. The final mechanism contains 664 species and 4587 reactions. This new mechanism (ALLNLB2_B_R) has been tested over IDT and compared to its previous version. Overall, the reduced mechanism is only 0.17 less accurate than its previous version on IDT, and the difference comes mainly from low temperature *n*-heptane and toluene. However, when considering only IDT above 1100 K (against 1000 K in previous calculation), almost no difference is observed (0.04). All IDT have been represented on two parity plots in **Figure 6**. In **Figure 6 (a)**, the two mechanisms have similar results. This is confirmed in **Figure 6 (b)**, where there is almost as much IDT above than below those calculated with ALLNLV2_B (328 IDT above / 390 IDT below). However, IDT that are below look more deviated, especially for medium values.
<figure><img src="image_8.png" /><figcaption>**Figure 6.** (a) Parity plot of the experimental versus simulated IDT for ALLNLV2_B and ALLNLV2_B_R. (b) Parity plot of ALLNLV2_B_R IDT deviation to ALLNLV2_B IDT.</figcaption></figure>

Open (empty) symbols represent IDT where the starting temperature is between 1000 and 1100 K while closed (filled) symbols represent IDT with a starting temperature above 1100 K.

# 5. Comparison between the present model and the newly-measured TRF-B flame data

## 5.1. Major species

The structure of an atmospheric TRF-B rich flame ($\Phi$=1.72) has been studied experimentally as presented above. This equivalence ratio, below the sooting threshold, allows to focus only on the gaseous products without any soot particle interference. The TRF-B molar blend is 35.25% iso-octane, 10.25% n-heptane, 33.35% toluene, and 21.15% iso-butanol. It was diluted into 69.00 % of $N_2$ and 6.50 % of $CH_4$. The flow rates are $1.966 \times 10^{-1} \text{ l}_n/\text{min}$ (normal liter per minute) for the TRF, $5.272 \times 10^{-2} \text{ l}_n/\text{min}$ for the iso-butanol, $2.166 \text{ l}_n/\text{min}$ for $O_2$, $6.8 \text{ l}_n/\text{min}$ for $N_2$ and $6.400 \times 10^{-1} \text{ l}_n/\text{min}$ for $CH_4$. The experimental method is described in Section 2, and full experimental data are available in Excel file in SM2. **Figure 7** presents the fuel and $O_2$ consumption, CO / $H_2$ production, $N_2$ and flame temperature profiles as a function of the HAB.
The temperature reaches approximately 1780 K and decreases as it approaches the stabilization plate. A close agreement between NO-LIF and thermocouple measurements was observed. Additionally, the utilization of the sampling probe has impact on the gradient zone of the flame. The simulations were done with ANSYS Chemkin-Pro software for a Premixed Burner-stabilized Flame reactor model (1D) with the measured disturbed temperature profile.

**Figure 7** shows that the consumption of reactants is well represented, although the model seems to underestimate reactivity near the burner and then predict too steep profiles. This behavior may be explained, in part, by the slight mismatch, which remains between the experimental and simulated temperature profiles in the gradient zone. The thermal impact of the sampling probe has been a much-discussed topic in the combustion community. As a first test, different interpolations (linear, cubic concave, cubic convex) between the first temperature points were considered and however no significant effects were observed. The formation of the main products (CO and H₂) is well predicted within the experimental uncertainties.
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Mole fraction profiles of TRF-B reactant (n-heptane, iso-octane, toluene and iso-butanol), methane, N₂, O₂, CO and H₂ with temperature profiles. Symbols represent the experiment. Solid lines represent the model. Highlight represents the experimental uncertainty. The last point of the temperature profile at 21 mm is the temperature measured on the stabilizing plate.</figcaption></figure>
## 5.2. Intermediate species profiles

For all intermediate species in this flame, to avoid considering probe disturbances that slightly broaden the experimental profiles of the intermediates, the discrepancy between experiment and simulation mentioned for each profile in text below is calculated relative to the peak mole fraction.

**Figure 8** presents all light non-oxygenated intermediate hydrocarbons measured, i.e. acetylene ($C_2H_2$), ethylene ($C_2H_4$), ethane ($C_2H_6$), allene ($a-C_3H_4$), propyne ($p-C_3H_4$), propene ($C_3H_6$), propane ($C_3H_8$), 1,3-butadiene ($C_4H_6$), iso-butene ($i-C_4H_8$), but-1-ene ($C_4H_8-1$) and hex-1-ene ($C_6H_{12}$). Some of these species are involved in soot-forming reactions. For example, $C_2H_2$ is involved in the HACA mechanism, which is one of the main growth mechanism for PAHs [59]. The intermediate species analysis of TRF-B flame investigated in this study revealed $C_2H_2$ as the most abundant species, with a peak mole fraction around 12000 ppm. Next abundant species are $C_2H_4$, $C_2H_6$, $C_3H_6$, and $i-C_4H_8$. Overall, there is a global agreement between the model and the experimental results. However, the model exhibits a specific behavior, also present in other combustion mechanisms in rich conditions, where $C_2H_2$ does not decrease as much as it should after reaching its maximum mole fraction and could be due to missing reactions for large PAHs. The good simulation of $C_2H_6$ is also important in this flame since this species is mainly produced by $CH_3$ combination, by a reaction whose rate constant is well known. This indicates that the amount of $CH_3$ radicals is correctly predicted by the model. $C_3H_6$ and $i-C_4H_8$ are known to be soot precursors, as most of the light unsaturated hydrocarbons, which participate in the formation of the first aromatic rings.
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Mole fraction profiles of measured light non-oxygenated intermediate species up to hex-1-ene. Symbols represent the experiment. Solid lines represent the model. Highlight represents the experimental uncertainty.</figcaption></figure>
Iso-butene is a main product of the thermal degradation of iso-octane and iso-butanol, and the fourth most abundant measured intermediate species. While the model accurately predicts the amount of propene, there is a little underestimation of iso-butene, with an EF of 1.73. The mechanism predicts the proportion of hex-1-ene, despite its low concentration (around 25 ppm). Hex-1-ene is a characteristic species of *n*-heptane thermal decomposition, since it is formed from one of the main *n*-heptyl radical. It should be noted, however, that the experimental profile seems to exhibit a double peak; the second peak arrives quite late, at around 2 mm, whereas hex-1-ene appears to be a primary product from *n*-heptane thermal decomposition. But this stable species was measured with a very low amount (25 ppm), its chemistry is expected to do not influence other species discussed in the present paper.

**Figure 9** presents aromatic intermediate, i.e. benzene (C<sub>6</sub>H<sub>6</sub>), ethyl benzene (C<sub>6</sub>H<sub>5</sub>C<sub>2</sub>H<sub>5</sub>), styrene (C<sub>6</sub>H<sub>5</sub>C<sub>2</sub>H<sub>3</sub>), and benzaldehyde (C<sub>6</sub>H<sub>5</sub>CHO). These species are linked to toluene and are very

<figure><img src="image_11.png" /></figure>

**Figure 9.** Mole fraction profiles of measured aromatic intermediate species. Symbols represent the experiment. Solid lines represent the model. Highlight represents the experimental uncertainty.
important for the formation of the first PAHs [60]. PAH measurements are still in progress and since their formation chemistry and analysis are more complex, these species will be published in a future paper. The model predicts the right order of magnitude for benzene, even if it is overpredicted by a factor of two. In the model, benzene is mainly formed by ipso-addition of an H-atom on the cycle and by reaction pathways involving the methyl-cyclopentadiene. Ethylbenzene is mainly formed by the combination involving methyl and benzyl radicals and is quickly converted into styrene. Note that the concentration of the latter is over-predicted while that of ethylbenzene is under-predicted with almost identical deviations from experimental data. The sum of the two predictions is however close to the sum of the two experimental values. Benzaldehyde is underpredicted by the model by a factor of two.

**Figure 10** presents all oxygenated intermediates measured (except benzaldehyde), i.e. formaldehyde (CH₂O), methanol (CH₃OH), acetaldehyde (CH₃CHO), acetone (CH₃COCH₃), propenal or acroleine (C₂H₃CHO), propanal (C₂H₅CHO) and methylpropenal or methacroleine (C₄H₆O). Most of them are highly toxic and they were detected in significant amounts in this study. All are well simulated by the model, with the exception of acetone and propanal. However, sensitivity analysis performed with the mechanism of this study and Merchant-2013 mechanism shows that the keto-enolic tautomerization of propenol into propanal is an important reaction. Moreover, this reaction has almost no impact on the other species, but this reaction increases by a factor of 3 the amount of propanal. The new propanal mole fraction is the one depicted in **Figure 10**, and shows still too low compared to the experiment. However, the sum of the enol and aldehyde isomers (shown on the same profile as the aldehyde) gives a correct result. It is likely that the experimental measurement corresponds to this sum, given the reactivity of
enols and the difficulty of separating the two isomers. This suggests the need for further investigations on enol-keto conversion for these C₃ species, and is a subject of future work.
**Figure 10.** Mole fraction profiles of measured oxygenated intermediate species. Symbols represent the experiment. Solid lines represent the model. Highlight represents the experimental uncertainty.

Acetone is mainly produced through two reaction routes. The major pathway comes from iso-octane that react in two steps into 2,4-dimethyl-2-pentene ($C_7H_{14}$). H-atom abstraction, from the methyl groups of $C_7H_{14}$, leads to a resonance-stabilized radical, which can react with $HO_2$ to form an alkoxy radical (+OH); this latter decomposes into acetone and the vinylic radical of isobutene (Figure 11).

<figure><img src="image_12.png" /><figcaption>**Figure 11.** Major reaction pathway from iso-octane to acetone.</figcaption></figure>

The other main pathway comes from an addition of OH on the double bond of isobutene and elimination of $CH_3$ followed by an isomerization in two steps into acetone. As the first pathway impacts the primary decomposition mechanism from iso-octane, this will need further investigation on the rate constants involved to not impact the overall reactivity of iso-octane in case of change. The other pathway has been investigated but it does not seem to be enough to come close to the experimental values.

Overall, the new mechanism has an average error factor of 1.66 on the intermediate species profile.

**6. Conclusion**
The first detailed kinetic mechanism of TRF-iso-butanol which has been validated on a wide range of experimental data has been developed. It was also validated successfully against the first detailed speciation of a TRF-B rich atmospheric flame. These rich flames are a good representation of the various problems found in direct-injection engines, including pool fires. The model represents with a correct agreement the consumption of the reactants and the formation of characteristic products of TRF-B, such as iso-butene, the main product of decomposition of iso-octane, and also one of that of iso-butanol, benzene and ethylbenzene, products of toluene, ethylene and propene products of *n*-heptane and propanal, the product of iso-butanol. The model accounts for reactivity of the proposed surrogate of oxygenated gasoline over extended conditions from the literature and specifically under very rich conditions. It can reproduce the formation of soot precursors. The perspectives associated with this work mainly concern the addition of a mechanism representing the formation of large PAHs in order to better predict soot formation. It should be validated against new PAHs experimental data point from TRF-B rich atmospheric flames, which is ongoing work.

**Declaration of competing interest**

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

**Acknowledgements**

This work was founded by the Agence Nationale de la Recherche in the project OFELIE (ANR-20-CE05-0047): Oxygenated FuELs impact on spark Ignition engine Emissions. High performance computing resources were provided by IDRIS under the allocation AD010812434R1 made by GENCI and also by the EXPLOR center hosted by the University of
Lorraine. The work at PC2A was also additionally founded by the project LABEX CAPPA (ANR-11-LABX-0005).

## Supplementary material

Supplementary materials SM1 (model information) and SM2 (experimental data) are submitted along with the manuscript.

## Author contributions

* TF: Software, Investigation, Validation, Visualization, Writing - Original Draft, Writing - Review & Editing

* DK: Investigation, Formal analysis, Data Curation, Writing - Original Draft, Writing - Review & Editing

* LG: Conceptualization, Methodology, Supervision, Writing - Review & Editing, Funding acquisition

* LST: Conceptualization, Methodology, Supervision, Writing - Review & Editing, Funding acquisition

* SG: Methodology, Investigation, Formal analysis.

* NL: Methodology, Investigation, Formal analysis.

* RF: Conceptualization, Methodology, Supervision, Writing - Review & Editing, Funding acquisition

* BS: Conceptualization, Methodology, Writing - Review & Editing

* PAG: Conceptualization, Methodology, Supervision, Writing - Review & Editing, Funding acquisition

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