Predicting Sooting Propensity of Oxygenated Fuels Using Artificial

processes

Article Predicting Sooting Propensity of Oxygenated Fuels Using Artiﬁcial Neural Networks

Abdul Gani Abdul Jameel

Department of Chemical Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia; [email protected]

Abstract: The self-learning capabilities of artificial neural networks (ANNs) from large datasets have led to their deployment in the prediction of various physical and chemical phenomena. In the present work, an ANN model was developed to predict the yield sooting index (YSI) of oxygenated fuels using the functional group approach. A total of 265 pure compounds comprising six chemical classes, namely paraffins (n and iso), olefins, naphthenes, aromatics, alcohols, and ethers, were

dis-assembled into eight constituent functional groups, namely paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic –CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, alcoholic OH groups, and ether O groups. These functional groups, in addition to molecular weight and branching index, were used as inputs to develop the ANN model. A neural network with two hidden layers was used to train the model using the Levenberg–Marquardt (ML) training algorithm. The developed model was tested with 15% of the random unseen data points. A regression coefficient (R2) of 0.99 was obtained when the experimental values were compared with the predicted YSI values from the test set. An average error of 3.4% was obtained, which is less than the experimental uncertainty associated with most reported YSI measurements. The developed model can be used for YSI prediction of hydrocarbon fuels containing alcohol and ether-based oxygenates as additives with a high degree of accuracy. Citation: Abdul Jameel, A.G. Predicting Sooting Propensity of Keywords: soot; ANN; alcohol; ether; functional group Oxygenated Fuels Using Artificial Neural Networks. Processes 2021, 9, 1070. https://doi.org/10.3390/ pr9061070 1. Introduction

Academic Editor: Adam Smoliński Carbonaceous particles known as soot, which are formed and emitted during the incomplete combustion of fossil fuels, have a negative impact on human health and the Received: 1 June 2021 environment. Airborne soot particles (<2.5 µm) have been shown to be one of the largest Accepted: 15 June 2021 cancer-causing air pollutants [1], and when inhaled can cause of a number of health issues, Published: 19 June 2021 such as bronchitis, asthma, and premature death. Soot also results in environmental issues, such as haze formation, reduced visibility, acidification of water bodies, and global warm- Publisher’s Note: MDPI stays neutral ing. Soot is second only to carbon dioxide in its potential to cause global warming [2–4] with regard to jurisdictional claims in because it heats the atmosphere by absorbing thermal radiation from the sun. Studies [5] published maps and institutional affil- have shown that limiting the emissions of particulate matter (PM) from fossil fuel combus- iations. tion can be one of the most effective ways of slowing the rate of global warming, and a net 20–45% reduction in global warming can be realized by eliminating PM emissions. Gasoline spark-ignited (SI) engines tend to produce lower soot compared to compression-ignited (CI) diesel engines [6]. However, due to the application of PM filters Copyright: © 2021 by the author. in the exhausts of diesel engines, PM emissions have been significantly reduced, to now be Licensee MDPI, Basel, Switzerland. less than those from gasoline engines [7–9]. Soot emitted from internal combustion (IC) This article is an open access article engines has a short lifetime of around a week and the positive impact of net soot reduction distributed under the terms and can be realized quickly. Soot formation in engines is often an impediment to meeting conditions of the Creative Commons the conflicting objectives of increased efficiency and lower emissions because advanced Attribution (CC BY) license (https:// combustion strategies such as gasoline direct injection (GDI), which aim towards maximiz- creativecommons.org/licenses/by/ ing fuel efficiency, may result in increased emissions. An effective and tenable approach 4.0/).

Processes 2021, 9, 1070. https://doi.org/10.3390/pr9061070 https://www.mdpi.com/journal/processes Processes 2021, 9, 1070 2 of 18

to reduce PM emissions from engines is by optimizing engine design to attain a balance between fuel efficiency and emissions. Another method is to blend the fuels with selective oxygenate additives, such as alcohols and ethers, that can reduce soot formation during combustion. A number of studies have shown that oxygenates, such as methanol [10–12], ethanol [10,13,14], n-butanol [15,16], n-octanol [17], dimethyl ether [18], polyoxymethylene dimethyl ether (PODE) [19–22], and diethyl ether [23–25], can reduce soot formation in IC engines. The propensity of oxygenated compounds to reduce soot is not only dependent on the oxygen content, but also strongly depends on the molecular structure, as shown by a number of works [26–31]. A theoretical model to predict the propensity of a fuel to form soot based on fuel composition and combustion conditions remains elusive due to the lack of soot mechanisms coupled with the complexity arising from turbulence–chemistry interactions. This has prompted the development and use of empirical models for quantifying sooting tendencies. The threshold sooting index (TSI)[32] is an artificially defined number that represents the tendency of a pure compound or a fuel mixture to form soot. TSI is measured using a standardized smoke point (SP) lamp, where the SP corresponds to the maximum height (in mm) of a smoke-free laminar non-premixed flame. The TSI is calculated from the measured SP of the fuel using Equation (1).

MW TSI = a + b (1) SP

where MW refers to the molecular weight of the fuel and the dimensionless notations, a and b are the constants of the experimental set-up provides the TSI, which is apparatus- independent. The TSI of a fuel is inversely proportional to the SP, and therefore a heavily sooting fuel has a lower SP compared to a fuel with a lower sooting propensity. One of the major drawbacks of using the TSI to quantify sooting is the uncertainty associated with the SP measurements, which are often prone to observational errors. Aromatic fuels, which have large TSIs, produce low smoke heights, which are difficult to measure and, to the contrary, paraffinic fuels, which have lower TSIs, generate larger smoke heights, which may be outside the measurement range of the standard lamps. The yield sooting index (YSI) is another method for measuring the sooting tendency, introduced by McEnally and Pfefferle [33], in which the fuel whose YSI is to be measured is doped with methane (base fuel) at ppm level concentrations. A non-premixed diffusion flame is then generated and the maximum soot volume fraction (fv,max) is measured at the centerline of the flame from which the YSI is calculated, as defined in Equation (2).

YSI = c × fv,max + d (2)

where c and d are experimental constants, established with a scale that assigns benzene with a YSI of 30 and 1,2-dihydronaphthalene with a YSI of 100. The advantage of using YSI is that soot volume fractions correlate closely with fuel sooting propensity and can be more accurately measured using laser-induced incandescence compared to SP, as in the case of TSI. Because only a small amount of the test fuel is used, there is no significant impact on the flame temperature, and the measured soot volume fraction effectively captures the effect of fuel molecular structure on sooting propensity. Experimental YSI measurements have been reported by McEnally and Pfefferle’s group for aromatic hydrocarbons [33], non-volatile aromatic hydrocarbons [34], large hydrocarbons [35], oxygenated hydrocarbons [31], unsaturated esters [36], gasolines and their surrogates [37], and diesel and jet fuel and their surrogates [38]. YSI measurements have been made using two scales with different sets of reference compounds. The “high scale” [31,34] uses benzene and naphthalene as index reference compounds with assigned YSI values of 30 and 100, respectively. The “low scale” [31,36] is indexed using n-hexane and benzene with YSI values of 0 and 100, respectively. Recently a “unified scale” [27] was introduced to resolve the incompatibility of the different scales, and a new database was created by measuring the YSI of a number Processes 2021, 9, 1070 3 of 18

of hydrocarbon and oxygenated compounds by combining the two scales. n-Hexane and benzene with YSI values of 30 and 100, respectively, were used as the reference compounds in the unified scale, which also benefitted from the application of color ratio pyrometry for soot volume measurements, and has a better range compared to earlier diagnostics. The objective of the present work was to develop an artificial neural network (ANN)- based model using the unified YSI database [27] that can predict the YSI of pure compounds and real fuels, containing the following chemical classes: paraffins (n and iso), olefins, naphthenes, aromatics, alcohols, and ethers. A large number of alternative fuels or additives blended with transportation fuels, such as gasoline or diesel, usually comprise alcohols and ethers [39], and therefore other oxygenated groups, such as aldehydes, ketones and esters, were not included in the model. An ANN was used as the tool for developing the model due to its potential to mathematically capture complex, non-linear behavior often noticed in chemical phenomenon. ANNs are a set of nodes that are interconnected with each other and resemble the neurons in the brain. ANNs work towards a unitary goal and attempt to fit a mathematical function based on fed inputs to predict one or more desired outputs. ANNs have the ability to recognize and learn the relationship between a set of inputs(s) and output(s) from a dataset, provided the ANN be trained correctly. The ANNs can be trained and adapted in a number of different ways by optimizing a number of hyper-parameters, such as the number of hidden layers, number of nodes in each layer, number of training cycles of the full dataset (known as epochs), and batch size. Detailed information on the theory and background of ANNs is reported in a number of works [40–46]; thus, the theory behind them is not discussed here. 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 [49–51].

2. Methodologies 2.1. Dataset Generation The dataset used for training the ANN model contains 265 pure compounds, which in turn consists of 30 paraffins, 32 olefins, 27 naphthenes, 101 aromatics, 40 alcohols, and 35 ether compounds. The pure compounds were disassembled into the following underlying structural moieties or functional groups: paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic –CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, alcoholic OH groups, and ether O groups. The functional group distribution in weight percent (wt%) of n-heptane, 1-heptene, ethylcyclopentane, toluene, 2-propanol, and methylbutyl ether is presented in Figure1. For example, methyl butyl ether (MW = 88 g/mol) has two paraffinic CH3 groups on either side of the molecule, three paraffinic CH2 groups, and an ether O group. The molecular weights of the paraffinic CH3 group, paraffinic CH2 group, and ether O group are 15, 14, and 16 g/mol, respectively. Therefore, the functional groups in methyl butyl ether are calculated as 34.1 wt% of paraffinic CH3 groups, 47.7 wt% of paraffinic CH2 groups, and 18.2 wt% of ether O groups. The other two input features, namely MW and BI, are calculated from the molecular structure of the compound. BI represents the degree of branching in a compound, in which both the size and position of the side chains are taken into account while computing the value. This parameter helps to distinguish isomers (for example, 2-methyl heptane and 3- methyl heptane) that have the same functional group distribution but have slightly different combustion properties, such as DCN, octane number, TSI, and YSI. The methodology used to calculate the BI of compounds has been previously explained in detail [47]. The entire dataset used in the present work, in addition to the input features and output of interest (YSI), are provided in the Supplementary Material. Processes 2021, 9, 1070 4 of 19

Processes 2021, 9, 1070 used to calculate the BI of compounds has been previously explained in detail [47].4 The of 18 entire dataset used in the present work, in addition to the input features and output of interest (YSI), are provided in the Supplementary Material.

Paraffinic CH3 Paraffinic CH2 Paraffinic CH Olefinic -CH=CH2 Naphthenic -CH-CH Aromatic C-CH Alcoholic OH Ether O 2 100 15.3 15.3 16.3 30 34.1 80 14.3 50

60 57.1

83.7 47.7 40 21.7 70 70.4

Functional%) groups (wt 20 27.6 28.3 18.2

toluene n-heptane 1-heptene 2-propanol ethylcyclopentane methylbutyl ether FigureFigure 1. 1. FunctionalFunctional group group distribution distribution of of some some compounds. compounds.

2.2.2.2. ANN ANN Development Development TheThe model model was developed developed using using the the neural neural network network (NN) (NN) toolbox toolbox available available in MAT- in MATLABLAB 2020a. 2020a. The The ANN ANN model model was was developed developed using using a feed a feed forward forward propagation propagation technique tech- niqueby employing by employing a multilayer a multilayer perceptron perceptron (MLP), (MLP) which, which is suitable is suitable for the for present the present supervised su- input-output learning problem. The topology of a MLP consists of nodes in an initial input pervised input-output learning problem. The topology of a MLP consists of nodes in an layer, a middle hidden layer, and a final output layer. Ten inputs (functional groups, MW initial input layer, a middle hidden layer, and a final output layer. Ten inputs (functional and BI) were specified as the inputs and YSI was specified as the output. The ANN was de- groups, MW and BI) were specified as the inputs and YSI was specified as the output. The veloped with two hidden layers. The topology of the ANN model is presented in Figure2 . ANN was developed with two hidden layers. The topology of the ANN model is pre- The dataset is split into three sets: a training set that consists of 70% of the data points, a sented in Figure 2. The dataset is split into three sets: a training set that consists of 70% of validation set containing 15% of the points, and a test set possessing the remaining 15% of the data points, a validation set containing 15% of the points, and a test set possessing the the data. The selection of points in these sets was randomly performed by the tool with remaining 15% of the data. The selection of points in these sets was randomly performed no manual interference. An inbuilt training technique, namely the Levenberg–Marquardt by the tool with no manual interference. An inbuilt training technique, namely the Leven- (ML) algorithm, was used to train the dataset due to its better performance. The mean berg–Marquardt (ML) algorithm, was used to train the dataset due to its better perfor- squared error (MSE) was chosen as the performance function and the architecture (number mance.of neurons The inm theean hiddensquared layer) error of (MSE) the ANN was was chosen varied as untilthe performance the required MSEfunction was obtainedand the architectureby trial and (number error. The of validation neurons setin the was hidden used for layer) assessing of the the ANN performance was varied of the until models the requduringired the MSE course was ofobtained model optimizationby trial and error. and the The test validation set was usedset was only used once for at assessing the end to thecheck performance the performance of the models of the final during model. the Thecourse parameters of model used optimization for the development and the test of set the waANNs used model only are once presented at the end in to Table check1. The the neuronsperformance in the of two the hiddenfinal model. layers The were parame variedters by used5, from for 5the to development 30, until satisfactory of the ANN results model were are obtained. presented in Table 1. The neurons in the two hidden layers were varied by 5, from 5 to 30, until satisfactory results were obtained.

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FigureFigure 2. 2. TopologyTopology of of the the ANN ANN model. model.

TableTable 1. 1. ANNANN parameters parameters employed employed for for model model development. development.

ANNANN Parameter Value ArchitectureArchitecture 1010× × 2525× × 2525 ×× 1 10 Inputs 10 Inputs (eight functional groups, MW and BI) Output(eight functional 1 (groups,YSI) MW and BI) TrainingOutput function Levenberg-Marquardt1 (YSI) (ML) TrainingPerformance function Levenberg-Marquardt MSE (ML) Error tolerance 0.0001 Performance MSE Number of epochs 250 Error tolerance 0.0001 Number of epochs 250 3. Results and Discussion

3. ResultsThe YSIandof Discussion an individual molecule is related to its constituent functional groups/ molecular structure as the measurement conditions, and the apparatus used were the same for allThe measurements. YSI of an individual The individual molecule impact is related of eachto its of constituent the input parameters/functionalfunctional groups/mo- leculargroups structure on YSI is as discussed the measurement in the following conditions sections., and the apparatus used were the same for all measurements. The individual impact of each of the input parameters/functional

groups3.1. Paraffinic on YSICH is discussed3 Groups in the following sections. Amongst the hydrocarbon chemical classes, paraffins possess the lowest YSI compared 3.1.to naphthenes, Paraffinic CH olefins,3 Groups and aromatics of similar carbon numbers. This can be demonstrated by usingAmongst the example the hydrocarbon of n-heptane, chemical which hasclasses, a YSI paraffinsof 36; this possess is the lowest the lowest value comparedYSI comparedto the sevento naphthenes, carbon counterparts olefins, and of aromaticsother chemical of similar classes. carbon 1-heptene, numbers. cycloheptane, This can and be demonstratedtoluene have higherby usingYSI thes, ofexample 48.4, 50, of and n-heptane 170.9, respectively., which has a YSI of 36; this is the lowest valueThe compared effect of to paraffinic the seven CH carbon3 groups counterparts on the YSI of compoundsother chemical in the classes. dataset 1-heptene, is shown cycloheptanein Figure3. For, and straight toluene chain have paraffins, higher YSI it cans, of be48.4, clearly 50, and observed 170.9, re (inspectively. red) that when the massThe contribution effect of paraffinic of the paraffinic CH3 groups CH 3ongroup the YSI increases, of compounds the YSI inis the reduced. dataset n-pentane, is shown inwhich Figure has 3. aFor paraffinic straight CHchain3 content paraffins, of 41.7 it can wt%, be clearly has a lower observedYSI of(in 24.6red)compared that when tothe n- masshexane contribution (paraffinic CHof the3 groups, paraffinic 34.9 CH wt%)3 group and n-heptane increases, (paraffinic the YSI is CHreduced3 groups,. n-p 30entane, wt%), whichwhich has have a higherparaffinicYSI CHs of3 30.4content and of 36, 41.7 respectively. wt%, has Brancheda lower YSI paraffins of 24.6 have compared higher toYSI n-s hexanecompared (paraffinic to n-paraffins CH3 groups, of the 34.9 same wt%) carbon and number.n-heptane For (paraffinic example, CH n-octane3 groups, has 30 a wt%) lower, whichYSI of have 42.6 comparedhigher YSI tos of 2-methylheptane 30.4 and 36, respectively. (YSI, 49.4) Branc andhed 2,2-dimethylhexane paraffins have higher (YSI, 52.8).YSIs comparedThis trend to is n true-paraffins for paraffins of the ofsame all carboncarbon numbers.number. For No example, definite trends n-octane are has observed a lower in YSIother of compound42.6 compared classes to 2 because-methylheptaneYSI is a net(YSI effect, 49.4) of and the 2,2 functional-dimethylhexane groups constituting (YSI, 52.8). Thisthe molecule.trend is true for paraffins of all carbon numbers. No definite trends are observed in other compound classes because YSI is a net effect of the functional groups constituting the molecule.

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0 0 0 10 20 30 40 50 60 0 10 20 30 40 50 60 70 Paraffinic CH (wt %) Paraffinic CH (wt %) 3 3 Figure 3. Effect of parafﬁnic CH groups on YSI. Figure 3. Effect of paraffinic CH33 groups on YSI.

3.2. Paraffinic CH2 Groups 3.2. Paraffinic CH2 Groups The paraffinic CH2 groups can be thought to represent the linearity of a molecule, as indicatedThe paraffinic by previous CH2 groups studies can [47 be,52 thought], and highto represent contents the of linearity this group of a indicates molecule the, as degreeindicated of by linearity. previous Lengthening studies [47,52] the, sideand high chains contents in aromatic of this molecules group indicates has been the showndegree toof increaselinearity. theirLengthening sooting propensitiesthe side chains [34 in]. aromatic This is primarily molecules due has to been the decompositionshown to increase of thetheir side sooting chains propensities to form hydrocarbon [34]. This radicals is primarily that react due withto the available decomposition benzylic of species the side to formchains naphthalene, to form hydrocarbon which eventually radicals leads that react to the with formation available of polyaromatic benzylic species heterocycle to form (PAH)naphthalene species., which eventually leads to the formation of polyaromatic heterocycle (PAH) species.The effect of paraffinic CH2 groups on YSI is presented in Figure4, and it can be observedThe effect that these of paraffinic groups enhance CH2 groups the sooting on YSI propensity is presented of n-paraffins.in Figure 4,Straight and it can chain be paraffins,observed that namely these n-pentane, groups enhance n-heptane, the n-nonane,sooting propensity and n-undecane, of n-paraffins. that have Straight increasing chain methyleneparaffins, namely content, n- exhibitpentane, increasing n-heptane,YSI n-nonane,values of and 24.6, n-undecane 36, 50.1, and, that 64.7, hav respectively.e increasing Thismethylene trend iscontent also observed, exhibit forincreasing some alkyl YSI aromatics, values of in24.6, which 36, 50.1 a higher, and methylene64.7, respectively content. inThis the trend side chainsis also resultsobserved in higherfor someYSI alkylvalues aromatics compared, into which aromatics a higher with methylene a lower degree content of

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in the side chains results in higher YSI values compared to aromatics with a lower degree linearityof linearity in thein the alkyl alkyl side side chains. chains. As anAs example,an example it can, it becan observed be observed that that n-propylbenzene n-propylben- haszene a has higher a higherYSI of YSI 235.7 of 235.7 compared compared to iso-propylbenzene, to iso-propylbenzene whose, whoseYSI is YSI 187.6. is 187.6. Similarly, Simi- ethylbenzenelarly, ethylbenzene has a larger has aYSI largervalue YSI of value 223.7 of when 223.7 compared when compared with both with 1,3-dimethylbenze both 1,3-dime- (thylbenzeYSI, 221.6) ( andYSI, 1,4-dimethylbenzene221.6) and 1,4-dimethylbenzene (YSI, 211.1). (YSI, 211.1).

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FigureFigure 4.4. EffectEffect ofof paraffinicparaffinic CHCH22 groupsgroups onon YSIYSI.. 3.3. Paraffinic CH Groups 3.3. Paraffinic CH Groups Sooting propensity is largely influenced by the degree of branching, and the presence Sooting propensity is largely influenced by the degree of branching, and the presence of paraffinic CH groups indicates branching in saturated molecules. Generally, branching, of paraffinic CH groups indicates branching in saturated molecules. Generally, branching, and hence paraffinic CH groups, tends to increase the sooting propensity, as indicated by a and hence paraffinic CH groups, tends to increase the sooting propensity, as indicated by number of works [53]. Figure5 shows the effect of these groups on YSI of the compounds ina number the dataset. of works 2,3-dimethylbutane, [53]. Figure 5 shows a six-carbonthe effect of atom these with groups 30.2 on wt% YSI of of the paraffinic compounds CH groups,in the dataset. has ahigher 2,3-dimethylbutane,YSI of 44 compared a six-carbon with both atom 2-methylpentane with 30.2 wt% of (YSI paraffinic, 36.7) and CH 3-methylpentanegroups, has a higher (YSI ,YSI 38.2) of that44 compared have lower with paraffinic both 2-methylpentane CH contents. No(YSI such, 36.7) trend and is3- observed in non-paraffinic molecules, as can be seen in Figure5.

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Processes 2021, 9, 1070 methylpentane (YSI, 38.2) that have lower paraffinic CH contents. No such trend 8is of ob- 18 served in non-paraffinic molecules, as can be seen in Figure 5.

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FigureFigure 5. 5.Effect Effect of of parafﬁnic paraffinic CH CH groups groups on onYSI YSI. .

3.4. Olefinic –CH=CH2 Groups 3.4. Olefinic –CH=CH2 Groups Olefins in general are not highly abundant in transportation fuels, such as gasoline and diesel.Olefins The in unsaturationgeneral are not added highly by theabundant double in bond transportation tends to increase fuels, thesuch ability as gasoline of the compoundand diesel. toThe form unsaturation soot, and henceadded olefins by the generallydouble bond have tends higher to increaseYSI values the compared ability of the to theircompound paraffinic to form counterparts. soot, and 1-heptenehence olefins has generally a higher YSIhaveof higher 48.4 compared YSI values to compared n-heptane, to whichtheir paraffinic has a YSI counterparts.of 36. Branched 1-heptene olefins has have a higher higher YSI sooting of 48.4 propensities compared comparedto n-heptane to , straightwhich has chain a YSI olefins. of 36. 1-hexene Branched has olefins the lowest haveYSI higherof a six-carbonsooting propensities olefin at 42.4 compared compared to tostraight 2-methyl-1-pentene chain olefins. 1 (YSI-hexene, 42.9), has 2-ethyl-1-butene the lowest YSI of (YSI a six, 45.6),-carbon 3-methyl-1-pentene olefin at 42.4 compared (YSI, 45.1),to 2-methyl 2,3-dimethyl-1-butene-1-pentene (YSI, ( YSI42.9),, 53.4), 2-ethyl etc.-1-butene (YSI, 45.6), 3-methyl-1-pentene (YSI, 45.1),Figure 2,3-dimethyl6 illustrates-1-butene the impact (YSI, of53.4) olefinic, etc. –CH=CH 2 groups on YSI. In olefins, we can observe that increasing mass contribution of the olefinic groups results in a reduction in the YSI. This opposing trend is observed because most of the olefinic compounds in the dataset have a single double bond whose contribution to the total reduces with changing molecular size and, in the present case, the olefinic content reduces numerically with increase in Processes 2021, 9, 1070 9 of 19

Figure 6 illustrates the impact of olefinic –CH=CH2 groups on YSI. In olefins, we can

Processes 2021, 9, 1070 observe that increasing mass contribution of the olefinic groups results in a reduction9 of 18in the YSI. This opposing trend is observed because most of the olefinic compounds in the dataset have a single double bond whose contribution to the total reduces with changing molecular size and, in the present case, the olefinic content reduces numerically with in- molecularcrease in molecular size. This size. shows This that shows other that parameters, other parameters such as, such molecular as molecular size, have size a, have more a pronouncedmore pronounced effect oneffectYSI onthan YSI the than oleﬁnic the olefinic groups groups themselves. themselves.

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0 0 0 10 20 30 40 50 0 10 20 30 40 50 Olefinic -CH=CH (wt %) Olefinic -CH=CH (wt %) 2 2 30 30 FigureFigure 6.6. EffectEffect ofof olefinicolefinic –CH=CH–CH=CH22 groups on YSI.. alcohols 3.5. Effect28 of Naphthenic CH-CH Groups 28 3.5. Effect of Naphthenic CH-CH22 Groups YSI InIn general,general, naphthenesnaphthenes havehave higherhigher sootingsooting propensitiespropensities comparedcompared toto paraffinsparaffins andand 26 26 olefinsolefins ofof thethe samesame carboncarbon number.number. TheThe additionaddition ofof naphthenicnaphthenic moleculesmolecules inin fuelsfuels resultsresults inin ananYSI increaseincrease in the number number and and size size of of the the sootYSI soot particles particles formed formed due due to to the the formation formation of of benzene24 rings and small PAH structures.24 Naphthenes with a double bond (such as benzene rings and small PAH structures. Naphthenes with a double bond (such as cyclo- cyclopentene and cyclohexene) promote the formation of soot more than saturated cyclic pentene and cyclohexene) promote the formation of soot more than saturated cyclic mol- molecules,22 as evident from their higher YSI values.22 Cyclopentene and cyclohexene possess ecules, as evident from their higher YSI values. Cyclopentene and cyclohexene possess higher YSI values of 80.5 and 62.2, respectively, compared to cyclopentane (YSI, 39.4) and higher YSI values of 80.5 and 62.2, respectively, compared to cyclopentane (YSI, 39.4) and cyclohexane20 (YSI, 42.7). Figure7 presents the20 effect of naphthenic CH-CH 2 groups on cyclohexane28 29(YSI30, 42.7).31 Figure32 33 7 34presents35 the effect28 of29 naphthenic30 31 32 CH33-CH342 groups35 on YSI YSI and no apparent trend can be observed from the entries in the dataset. Naphthenes and no apparentOlefinic trend -CH=CH2 can (wt be %) observed from the entries Olefinicin the -CH=CH2 dataset. (wt %) Naphthenes have have significantly lower YSI values compared to aromatics of the same carbon number significantly lower YSI values compared to aromatics of the same carbon number and and rings. rings. 3.6. Effect of Aromatic C-CH Groups Aromatic hydrocarbons present in transportation fuels are primarily responsible for the majority of soot formed during combustion. It is now well established that aromatic molecules produce more soot compared to paraffins, because aromatics rings may stay intact and enable the growth of PAH precursors. Aromatic C-CH groups also have a significant impact on the ignition delay time, cetane number, and octane number, in addition to YSI. Aromatic fuels such as toluene have high ignition delay times (>100 ms) compared to paraffins, which have shorter ignition delays. As a result, aromatic species have larger octane numbers compared to paraffins and olefins. Processes 2021, 9, 1070 10 of 19 18

200 1400 naphthenes aromatics 1200

150 1000

800 100 YSI YSI 600

400 50

200

0 0 0 20 40 60 80 100 0 10 20 30 40 50 60 Naphthenic CH-CH (wt %) Naphthenic CH-CH (wt %) 2 2 50 aromatics FigureFigure1000 7. 7. EffectEffect of of naphthenic naphthenic CH CH-CH-CH22 groupsgroups on on YSIYSI.. alcohols Diesel is known to possess about 10–25%40 aromatic content by weight, whereas gaso- 3.6. 800Effect of Aromatic C-CH Groups lines contain a higher proportion, of around 20–50% [54]. A number of studies [55] reported Aromatic hydrocarbons present in transportation fuels are primarily responsible for in the literature have investigated the impact30 of aromatics on soot emission and characteri- the 600majority of soot formed during combustion. It is now well established that aromatic zation. Aromatic flames result in the formation of soot particles and precursors that are moleculesYSI produce more soot compared to YSI paraffins, because aromatics rings may stay structurally and morphologically more complex20 compared to soot produced from paraffinic intact400 and enable the growth of PAH precursors. Aromatic C-CH groups also have a sig- fuels [56]. Aromatic molecules in the dataset have very large YSI values compared to other nificant impact on the ignition delay time, cetane number, and octane number, in addition hydrocarbon200 and oxygenated compounds.10 1,2-diphenylbenzene has the largest YSI, of to1338.9, YSI. Aromatic in the present fuels dataset,such as toluene followed have by high pyrene ignition at 1250.1. delay Aromatic times (>100 PAH milliseconds) precursors compared to paraffins, which have shorter ignition delays. As a result, aromatic species are usually0 formed at the lower end of the flame0 height, and the size of the soot particles havegrows larger0 by the octane10 addition 20numbers of30 methyl compared40 and methylene50 to paraffins0 groups and10 olefins. that20 are added30 towards40 50 the tip of Olefinic -CH=CH (wt %) Olefinic -CH=CH (wt %) the flameDiesel [56 is]. known to possess2 about 10–25% aromatic content by 2weight, whereas gaso- lines30 Thecontain effect a higher of aromatic proportion C-CH, groupsof around on30 YSI20–50is% shown [54]. A in number Figure8 .of A studies general [5 trend5] reported in the literature have investigated the impact of aromatics on soot emission and of increasing YSI alcoholswith increasing aromatic content can be observed and this is more characterization.pronounced28 for aromaticAromatic compounds flames result that in havethe28 formationYSI values of insoot the particles range of and 800–1300. precursors The thataromatic are structurally compounds and present morphological in the datasetly more are complex highly YSI diverse, compared ranging to soot from produced a single from ring paraffinicbenzene26 to fuels a fused [56]. four Aromatic ring pyrene. molecules Similarly, in the26 aromatics dataset have with very short large alkyl YSI chains values (toluene) compared to other hydrocarbon and oxygenated compounds. 1,2-diphenylbenzene has the

Processes 2021, 9, 1070 to longYSI alkyl chains (n-tetradecylbenzene) areYSI also present. These diverse molecules11 in of the 19

largestdataset24 YSI increase, of 1338.9 the prediction, in the present range da andtaset the, followed24 accuracy by of pyrene the resulting at 1250.1. model. Aromatic PAH precursors are usually formed at the lower end of the flame height, and the size of the soot

particles140022 grows by the addition of methyl and22 methylene groups that are added towards the tip of the flame [56].aromatics 1200 20The effect of aromatic C-CH groups on YSI20 is shown in Figure 8. A general trend of 28 29 30 31 32 33 34 35 28 29 30 31 32 33 34 35 increasing1000 YSI with increasing aromatic content can be observed and this is more pro- Olefinic -CH=CH2 (wt %) Olefinic -CH=CH2 (wt %) nounced for aromatic compounds that have YSI values in the range of 800–1300. The aro- matic800 compounds present in the dataset are highly diverse, ranging from a single ring

benzeneYSI 600 to a fused four ring pyrene. Similarly, aromatics with short alkyl chains (toluene) to long alkyl chains (n-tetradecylbenzene) are also present. These diverse molecules in the 400 dataset increase the prediction range and the accuracy of the resulting model. 200

0 0 20 40 60 80 100 Aromatic C-CH (wt %) Figure 8. Effect of aromatic C-CHC-CH groups onon YSIYSI..

3.7. Effect of Alcohol OH Groups Linear alcohols such as ethanol and butanol havehave beenbeen widelywidely usedused asas fuelfuel additivesadditives inin transportationtransportation fuels. fuels. Branched alcohols, howeverhowever,, have a higher higher sooting sooting propensity propensity compared to n-parafﬁnsn-paraffins ofof thethe same same carboncarbon number.number. ForFor example,example, 2-methyl-2-butanol2-methyl-2-butanol ((YSI,, 32.9)32.9) andand 2-methyl-2-pentanol2-methyl-2-pentanol (YSI,, 36.4)36.4) havehave higherhigher YSIYSIss thanthan theirtheir n-parafﬁnicn-paraffinic counterparts, namely n-hexane (YSI, 30.4) and n-heptane (YSI, 36), respectively. This can be explained by the reaction of the elimination of water from 2-butanol, in which there is a reversal of the partial oxidation of the carbon atom connected with the OH group, and is followed by the formation of butene [31]. Reduction in soot after blending with branched alcohols has been reported by a number of studies. This phenomenon is primarily due to the fact that that the blended alcohol has a lower carbon number compared to the fuel it has replaced and so produces a net soot reduction. Figure 9 presents the effect of the alcoholic OH group on YSI, and it can be seen that an increase in the OH groups results in a steady reduction in the YSI, as anticipated.

70 50 alcohols ethers 60 40

30 40

YSI 30 YSI 20

10 10

0 0 0 10 20 30 40 50 60 0 10 20 30 40 50 Alcoholic OH (wt %) Alcoholic OH (wt %) 50 Figure 9. Effect of alcoholicaromatics OH groups on YSI. 1000 alcohols

3.8. Effect of Ether O Groups 40 800 For a given carbon number, ether molecules possess the lowest YSI compared to al- 30 cohol600 or other hydrocarbon compounds in the dataset. As a result, ether compounds, such

as YSI dimethyl ether, diethyl ether, and methyl YSI tert-butyl ether (MTBE), have found applica- 20 tion400 as fuel additives that can lower soot emissions and increase combustion efficiency. One of the causes of the low sooting propensity of ether compounds is the ether O atom, which200 interrupts the carbon chain, thus result10 ing in smaller reaction products that are

0 0 0 10 20 30 40 50 0 10 20 30 40 50 Olefinic -CH=CH (wt %) Olefinic -CH=CH (wt %) 2 2 30 30

alcohols 28 28

YSI

26 26

YSI YSI

24 24

22 22

20 20 28 29 30 31 32 33 34 35 28 29 30 31 32 33 34 35 Olefinic -CH=CH2 (wt %) Olefinic -CH=CH2 (wt %) Processes 2021, 9, 1070 11 of 19

1400 aromatics 1200

1000

800

YSI 600

400

200

0 0 20 40 60 80 100 Aromatic C-CH (wt %) Figure 8. Effect of aromatic C-CH groups on YSI.

3.7. Effect of Alcohol OH Groups Linear alcohols such as ethanol and butanol have been widely used as fuel additives Processes 2021, 9, 1070 in transportation fuels. Branched alcohols, however, have a higher sooting propensity11 of 18 compared to n-paraffins of the same carbon number. For example, 2-methyl-2-butanol (YSI, 32.9) and 2-methyl-2-pentanol (YSI, 36.4) have higher YSIs than their n-paraffinic counterpartscounterparts,, namely namely n n-hexane-hexane (YSI (YSI, ,30.4) 30.4) and and n n-heptane-heptane (YSI (YSI, ,36), 36), respectively. respectively. This This can can bebe explained explained by by the the reaction reaction of of the the elimination elimination of water from 2-butanol,2-butanol, inin whichwhich therethere is is a areversal reversal ofof thethe partial partial oxidation oxidation of of the the carbon carbon atom atom connected connected with with the the OH OH group, group and, and is isfollowed followed by by the the formation formation of of butene butene [ 31[31].]. ReductionReduction in in soot soot after after blending blending with with branched branched alcohols alcohols has been has reported been reported by a num- by a bernumber of studies. of studies. This phenomenon This phenomenon is primarily is primarily due to the due fact to thethatfact that that the thatblended the blendedalcohol hasalcohol a lower has carbon a lower number carbon numbercompared compared to the fuel to theit has fuel replaced it has replaced and so andproduces so produces a net soota net reduction. soot reduction. Figure Figure9 presents9 presents the effect the of effect the ofalcoholic the alcoholic OH group OH groupon YSI on, andYSI it, can and beit seen can bethat seen an thatincrease an increase in the OH in thegroups OH groupsresults in results a steady in a r steadyeduction reduction in the YSI in, the as an-YSI, ticipated.as anticipated.

70 50 alcohols ethers 60 40

30 40

YSI 30 YSI 20

10 10

0 0 0 10 20 30 40 50 60 0 10 20 30 40 50 Alcoholic OH (wt %) Alcoholic OH (wt %) 50 FigureFigure 9. 9. EffectEffect of of alcoholic alcoholicaromatics OH OH groups groups on on YSIYSI. . 1000 alcohols

3.8.3.8. Effect Effect of of Ether Ether O O Groups Groups 40 800 ForFor a agiven given carbon carbon number, number, ether ether molecules molecules possess possess the thelowest lowest YSIYSI comparedcompared to al- to alcohol or other hydrocarbon compounds in the30 dataset. As a result, ether compounds, such cohol600 or other hydrocarbon compounds in the dataset. As a result, ether compounds, such as dimethyl ether, diethyl ether, and methyl tert-butyl ether (MTBE), have found application Processes 2021, 9, 1070 as YSI dimethyl ether, diethyl ether, and methyl YSI tert-butyl ether (MTBE), have found applica-12 of 19 as fuel additives that can lower soot emissions20 and increase combustion efﬁciency. One of tion400 as fuel additives that can lower soot emissions and increase combustion efficiency. the causes of the low sooting propensity of ether compounds is the ether O atom, which One of the causes of the low sooting propensity of ether compounds is the ether O atom, interrupts the carbon chain, thus resulting in10 smaller reaction products that are unlikely to which200 interrupts the carbon chain, thus resulting in smaller reaction products that are unlikelyform soot to precursors. form soot Dimethoxymethane,precursors. Dimethoxymethane, a three-carbon a etherthree- compound,carbon ether has compound the lowest,

hasYSI the0of anlowest ether YSI compound of an ether in thecompound dataset, in at 0the 10.9, dataset whereas, at isoamyl10.9, whereas ether, isoamyl which has ether ten, whichcarbon0 has atoms, ten10 carbon possessed20 atoms30 the, possess largest40 YSIed50 thevalue largest0 of 63.6. YSI10 Thevalue20 effect of 63.6. of30 ether The40 effect O groups50 of ether on the O Olefinic -CH=CH (wt %) Olefinic -CH=CH (wt %) groupsYSI is shown on the inYSI Figure is shown 102, and in Figure it can be10, observed and it can that, be observed as the ether that2 content, as the increases,ether content the 30 30 increasesYSI of the, the compounds YSI of the decreases. compounds decreases.

alcohols 7028 28 ethers YSI 60 26 26

YSI YSI

24 24 40

YSI 3022 22

20 20 20 28 29 30 31 32 33 34 35 28 29 30 31 32 33 34 35 10 Olefinic -CH=CH2 (wt %) Olefinic -CH=CH2 (wt %)

0 0 10 20 30 40 50 60 Ether O (wt %) FigureFigure 10. 10. EffectEffect of of ether ether O O groups groups on on YSIYSI..

3.9. Effect of Molecular Weight It is well established in the literature that sooting propensity of a compound increases with molecular weight. Experiments performed on a diesel engine using blends of n-decane and n-hexadecane have shown that increasing the molecular weight of the fuel by increasing the blend ratio of n-hexadecane led to an increase in soot formation [57]. The molecular weight of a fuel influences its physical properties such as volatility, and Makwana et al. [58] showed that fuel volatility correlates positively with soot volume fractions produced in n-heptane/n-hexadecane mixtures employing both premixed and non- premixed flames. The effect of molecular weight on the YSI of the compounds is shown in Figure 11, and it can be noted that the sooting propensity of compounds of all chemical classes increases with an increase in the molecular size.

Processes 2021, 9, 1070 12 of 18

3.9. Effect of Molecular Weight It is well established in the literature that sooting propensity of a compound increases with molecular weight. Experiments performed on a diesel engine using blends of n- decane and n-hexadecane have shown that increasing the molecular weight of the fuel by increasing the blend ratio of n-hexadecane led to an increase in soot formation [57]. The molecular weight of a fuel inﬂuences its physical properties such as volatility, and Makwana et al. [58] showed that fuel volatility correlates positively with soot volume fractions produced in n-heptane/n-hexadecane mixtures employing both premixed and Processes 2021, 9, 1070 non-premixed ﬂames. The effect of molecular weight on the YSI of the compounds13 of is19 shown in Figure 11, and it can be noted that the sooting propensity of compounds of all chemical classes increases with an increase in the molecular size.

100

100 n-paraffins olefins iso-paraffins 90

80 80

70 60

YSI YSI 60 40 50

20 40

0 30 0 50 100 150 200 0 50 100 150 200 Molecular weight Molecular weight

1400 naphthenes aromatics 200 1200

1000 150

800

YSI 100 YSI 600

400 50 200

0 0 0 50 100 150 200 0 50 100 150 200 250 300 Molecular weight Molecular weight

70 ethers alcohols 70 60 60 50 50 40 40

YSI 30 YSI 30

20 20

10 10

0 0 0 30 60 90 120 150 0 30 60 90 120 150 Molecular weight Molecular weight

FigureFigure 11.11. EffectEffect ofof molecularmolecular weightweight onon YSIYSI..

3.10. Effect of Branching Index The effect of molecular branching on combustion properties, such as ignition delay, octane number, and cetane number, have been studied in a number of works. Recently, Abdul Jameel et al. [47] defined the term “branching index”, which quantifies the degree of branching of paraffins, olefins, naphthenes, and aromatics, by including the effect of the position of the alkyl branch in the molecule. For example, 2-methylpentane and 3- methylpentane have the same degree of branching and the same functional groups. How- ever, their physical, chemical, and combustion properties are different. 2-methylpentane has a YSI of 36.7, whereas 3-methylpentane has a YSI of 38.2. The difference in the YSI values of these compounds can be explained by their different branching indexes. 2- methylpentane has a branching index of 0.2, whereas 3-methylpentane has a branching index of 0.3 due to the methyl branch in position 3, which is one position away from the outermost position on the chain. The definition of the BI term includes both the size and

Processes 2021, 9, 1070 13 of 18

3.10. Effect of Branching Index The effect of molecular branching on combustion properties, such as ignition delay, octane number, and cetane number, have been studied in a number of works. Recently, Abdul Jameel et al. [47] defined the term “branching index”, which quantifies the degree of branching of paraffins, olefins, naphthenes, and aromatics, by including the effect of the position of the alkyl branch in the molecule. For example, 2-methylpentane and 3-methylpentane have the same degree of branching and the same functional groups. However, their physical, chemical, and combustion properties are different. 2-methylpentane has a YSI of 36.7, whereas 3-methylpentane has a YSI of 38.2. The difference in the YSI values of these compounds can be explained by their different branching indexes. 2-methylpentane has a branching index of 0.2, whereas 3-methylpentane has a branching index of 0.3 due to the methyl branch in position 3, which is one position away from the outermost position on the chain. The definition of the BI term includes both the size and the position of the alkyl branches, which helps in differentiating these structural isomers. Providing more information on the branching index is outside the scope of the present work and detailed information can be obtained elsewhere [47,59]. Branching tends to increase the sooting propensity of compounds of all chemical classes, as shown by a number of works [30,38,53]. Figure 12 shows the impact of branching index on YSI and, as expected, it can be discerned that the YSI increases with an increase in the branching index.

3.11. ANN Model The ANN model for YSI prediction was developed as per the methodology described in Section 2.2. A number of parameters, such as batch size, epochs, number of hidden layers, and number of neurons in each hidden layer, were optimized to yield the final ANN model. Nearly 260 iterations were carried out and the best performance was observed in the 250th iteration. Figure 13 shows the comparison of the measured and predicted YSI for the entries in the training set, test set, and the entire dataset. It can be seen that the regression coefficient is close to unity for all three cases. It is especially encouraging to see the high level of correlation (R2 = 0.99) in the test set, which was unseen by the model until it was tested at the end. The developed YSI model has an average error of 3.4%, which is less than the experimental error observed in most cases. This shows the ability of ANN- based models to predict complex chemical phenomena, such as sooting propensity. This also supports the functional group-based approach employed here, which reiterates the ability of the functional groups to predict fuel properties, as previously demonstrated for cetane number [47,48] and octane number [44]. Another advantage of the functional group approach is the ability to predict the YSI of real fuels, which most other models reported in the literature are not designed to do. The functional groups present in hydrocarbon fuels, such as gasoline and diesel, can be identified and quantified using techniques such as nuclear magnetic resonance (NMR) spectroscopy [47,60,61]. The oxygenated functional groups, such as alcohol OH and ether O groups, can be calculated from the blending ratio of oxygenates added to the petroleum fuels. Processes 2021, 9, 1070 14 of 19

the position of the alkyl branches, which helps in differentiating these structural isomers. Providing more information on the branching index is outside the scope of the present work and detailed information can be obtained elsewhere [47,59]. Branching tends to increase the sooting propensity of compounds of all chemical classes, as shown by a number Processes 2021, 9, 1070 of works [30,38,53]. Figure 12 shows the impact of branching index on YSI and,14 as of ex- 18 pected, it can be discerned that the YSI increases with an increase in the branching index.

100

100 n-paraffins olefins iso-paraffins 90

80 80

70 60

YSI YSI 60 40 50

20 40

0 30 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Branching index Branching index

1400 naphthenes aromatics 200 1200

1000 150

800

YSI 100 YSI 600

400 50 200

0 0 0.0 0.2 0.4 0.6 0.8 1.0 0 1 2 3 4 Branching index Branching index

70 ethers alcohols 70 60 60 50 50 40 40

YSI 30 YSI 30

20 20

10 10

0 0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Branching index Branching index

FigureFigure 12.12.Effect Effect ofof branchingbranching indexindex ononYSI YSI. .

Processes 2021, 9, 1070 15 of 19

served in the 250th iteration. Figure 13 shows the comparison of the measured and predicted YSI for the entries in the training set, test set, and the entire dataset. It can be seen that the regression coefficient is close to unity for all three cases. It is especially encouraging to see the high level of correlation (R2 = 0.99) in the test set, which was unseen by the model until it was tested at the end. The developed YSI model has an average error of 3.4%, which is less than the experimental error observed in most cases. This shows the ability of ANN-based models to predict complex chemical phenomena, such as sooting propensity. This also supports the functional group-based approach employed here, which reiterates the ability of the functional groups to predict fuel properties, as previously demonstrated for cetane number [47,48] and octane number [44]. Another advantage of the functional group approach is the ability to predict the YSI of real fuels, which most other models reported in the literature are not designed to do. The functional groups present in hydrocarbon fuels, such as gasoline and diesel, can be identified and quantified using techniques such as nuclear magnetic resonance (NMR) spectroscopy Processes 2021, 9, 1070[47,60,61]. The oxygenated functional groups, such as alcohol OH and ether O groups, can 15 of 18 be calculated from the blending ratio of oxygenates added to the petroleum fuels.

Figure 13. ComparisonFigure 13. Compar betweenison the between measured the and measured predicted and values predicted of YSI valuesin the of training YSI in set,the testtraining set, andset, entiretest dataset. set, and entire dataset. 4. Conclusions In the present work, an ANN model was developed to predict the YSI values of fuels encompassing the following six chemical classes: paraffins, olefins, naphthenes, aromatics, alcohols, and ethers. The compounds were segregated into their constituent functional groups or molecular moieties, and these were used as input criteria for training the ANN model, in addition to molecular weight and branching index. The ANN model was developed using MATLAB 2020a by training on 70% of the dataset, which was randomly generated. The ANN model was trained using the Levenberg–Marquardt (ML) algorithm using 15% of the data for validating the generated models, and the mean squared error (MSE) was specified as the loss function. The final model was tested with 15% of the data, which was initially extracted from the dataset and kept aside for model testing. Aromatic groups and branching index had the most significant correlation with YSI, whereas an increase in alcoholic OH and ether O groups led to a steady reduction in the sooting propensity. A regression coefficient (R2) of 0.99 was obtained between the measured and predicted YSI values, which indicates the ability of the ANN model to accurately predict the YSI of random compounds, whose YSI values varied within a large range from 10 to 1300. The average error obtained for the test set was 3.4%, which is lower than the experimental error associated with measurements. The developed model can successfully predict the YSI of pure compounds possessing the discussed functional groups. It can also be applied to YSI prediction of real fuels if the fuel functional groups and branching index Processes 2021, 9, 1070 16 of 18

can be estimated using advanced spectroscopic techniques. The other input feature, namely molecular weight, can be obtained using chromatographic methods or distillation curves. The present model can also be used for screening purposes when a large volume of fuels needs to be tested. The model can also predict the sooting tendency of novel molecules that are not available in the dataset, based on their functional groups, as demonstrated by the results.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/pr9061070/s1. Funding: The author would like to acknowledge the support received from the Deanship of Scientific Re-search (DSR) at King Fahd University of Petroleum & Minerals (KFUPM), Saudi Arabia under University funded Grant # SR191020. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Acknowledgments: The author would like to acknowledge the support received from the Deanship of Scientific Research (DSR) at King Fahd University of Petroleum & Minerals (KFUPM), Saudi Arabia under University funded Grant # SR191020. Conflicts of Interest: The author declares no conflict of interest.

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