Recent studies on soot modeling for diesel combustion

Hamid Omidvarborna a, Ashok Kumar a, Dong-Shik Kim b,n

a Department of Civil Engineering, The University of Toledo, Toledo, OH, USAb Department of Chemical and Environmental Engineering, The University of Toledo, Toledo, OH, USA

a r t i c l e i n f o

Article history:Received 20 August 2014Received in revised form15 March 2015Accepted 3 April 2015Available online 28 April 2015

Keywords:Soot formationParticulate matterCombustion modelingPrecursor formationOxygenated fuel

a b s t r a c t

This paper analyzes published works on the emission models of diesel and BD1 fuels. To the best ourknowledge, this is the first comprehensive survey that reviews various modeling aspects of soot emittedfrom the combustion of diesel and BD fuels. The pros and cons of past and recent soot models, thechronological advancement of diesel combustion chemistry, and soot modeling approaches are high-lighted in this review. Soot models are divided into three main groups of empirical, semi-empirical, anddetailed soot model. Phenomenological model is also explored as a soot model which is one of the mostextensively investigated soot models in recent years. Soot formation mechanism is discussed with anemphasis on their molecular structure. In a vast majority of the papers reviewed, acetylene was used as asoot precursor, and also as a reactant for soot mass growth and aromatics formation in diesel sootmodeling studies. Thus, it is recommended that the formation and consumption of acetylene andaromatic compounds should be included in the diesel soot modeling. For BD, aromatic compounds arefound at very low concentrations during the combustion, so the contribution of aromatic compounds tosoot formation may be reduced or excluded in BD soot modeling. Unlike diesel, oxygen in BD fuels isfound very important in soot oxidation, thus, formation and consumption of oxygen molecules, radicalsand OH2 should be incorporated in the soot modeling as well. Finally, regardless of their structures,simple molecules such as MB3 and MD4 are found practical as BD surrogates in many modeling papers.

Published by Elsevier Ltd.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6362. Soot composition and structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6363. Soot formation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637

3.1. Precursors for soot formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6373.2. Nucleation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6383.3. Mass growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6383.4. Coagulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6383.5. Oxidation process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 638

4. Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6384.1. Empirical and semi-empirical soot models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6394.2. Recent studies on soot modeling with emphasis on phenomenological studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6404.3. Soot formation mechanism from oxygenated fuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 643

Contents lists available at ScienceDirect

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Renewable and Sustainable Energy Reviews

http://dx.doi.org/10.1016/j.rser.2015.04.0191364-0321/Published by Elsevier Ltd.

n Corresponding author. Tel.: þ1 419 530 8084; fax: þ1 419 530 8086.E-mail address: dong.kim@utoledo.edu (D.-S. Kim).1 Biodiesel.2 Hydroxide bonds.3 Methyl butanoate.4 Methyl decanoate.

Renewable and Sustainable Energy Reviews 48 (2015) 635–647

5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644Acknowledgement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645

1. Introduction

The call for emission reduction has been mandated by manygovernments. For past few years, it has been one of the top prioritiesfor combustion research centers to investigate combustion processesand emission reduction methods through optimizing the engines andfuels [1–3]. Soot modeling is regarded as an important part ofunderstanding the process of soot formation, which in turn contri-butes to the development of effective emissions reduction techniques.

Diesel is known as a source of emission species such asparticulate matter (PM), polycyclic aromatic hydrocarbons (PAHs),heavy metals, and nitrogen oxide (NOx) [4,5]. These species areproduced from combustion and found in emissions mainly in theform of aerosols, and are recognized as health hazards [6,7].Among these diesel emission components, PM5 has been a seriousconcern for human health due to its direct and broad impact onthe respiratory organs [4,5]. In earlier times, health professionalsassociated PM10 (diametero10 mm) with chronic lung disease,lung cancer, influenza, asthma, and the cause of increased mor-tality rate [8]. However, recent scientific studies suggest that thesecorrelations be more closely linked with fine particles (PM2.5) andultra-fine particles (PM0.1) [9], because the fine and ultra-fineparticles can easily penetrate deep into the lungs. To address theseproblems, a great deal of air quality research has been performedon toxicity and chemistry of PM over the last 40 years [10,11]. It isgenerally reported that the majority of PM is originated from soot,(highly carbonaceous material which weighs typically higher than50% in PM mass), which is usually formed in fuel-rich or low-oxygen regions of a diesel engine [12–15].

A better understanding of the soot formation made it possibleto formulate mathematical models that predict the concentrationor mass of soot in the emissions, and validate the proposed mech-anisms, and in turn good models are helpful in better under-standing soot characteristics and formation mechanisms. Signifi-cant advances have been made on the mechanisms of sootformation in the last two decades [16–19]. However, it appearsthat there is still a gap between the existing soot models andactual soot formation processes. The gap becomes even greaterwhen it comes to soot formation from combustion of oxygenatedfuels (BD fuels) due to the varying compositions and diverse typesof feedstock [20–22].

Soot modeling can be improved as long as the formation andoxidation mechanisms are clearly understood and accordinglymore realistic assumptions are made. This review begins with abrief description of soot formation fundamentals and summarizesthe progress of soot modeling for diesel combustion since the early1970s, while giving more emphasis on the modeling work over thelast 20 years. It is followed by different modeling approaches andcomparison of those approaches along with the highlights oftheoretical and empirical results. It also investigates the models'specifications and parameters, and then the accuracy of theirpredictions for the performance of the model with regard to thesoot concentration. Not all of the available models in the literatureare considered here, but the review focus is mainly on the practicalsoot models for vehicle combustion of diesel developed over thepast two decades.

2. Soot composition and structure

To better understand the mechanisms of soot formation foundin the literature, it is worthwhile to briefly review the chemistry ofthe soot. Soot is a solid substance consisting of roughly eight partsof carbon and one part hydrogen (soot density is 1.84701 g/cm3

[23] and the reports by most other authors fall near this value). Inurban areas soot is mostly formed as a result of fuel combustion inengines and its characteristics do not appear to be functions of fueland other operating conditions [24]. Soot becomes a part of blackcarbon/smoke when present in sufficiently large particle size andquantity in exhaust gases. Soot nucleates from the vapor phase toa solid phase in fuel-rich regions at elevated temperatures [13,14].HCs or other available molecules may condense on, or be absorbedby soot depending on the surrounding conditions [25].

A newly formed soot particle initially has the highest hydrogencontent, and the C/H ratio is as low as one. However, as the sootmatures, the carbon fraction increases. Trace amounts of zinc,phosphorus, calcium, iron, silicon, and chromium are also oftendetected in emitted soot from diesel engines [13,14,26,27].

Soot is found to be in the size of sub-microns and in the form ofnecklace-like agglomerates [16]. Fig. 1a is a typical scanningelectron microscope (SEM) image of diesel soot showing theseagglomerates are composed of collections of smaller particle unitsin spherical or close to spherical shape [17]. X-ray diffraction(XRD), as illustrated in Fig. 1b [28], indicates that the carbon atomsof a primary soot particle are packed into hexagonal face-centeredarrays, commonly referred to as platelets. Platelets are arranged inlayers to form crystallites, and there are typically two to fiveplatelets per crystallite [28]. When analyzed under high-resolutiontransmission electron microscopy (HRTEM), two distinct parts of aprimary diesel soot particle can be identified: an outer shell and aninner core, as shown in Fig. 1c [29]. The platelet model mentionedabove applies to the outer shell. However, the inner core containsfine particles with a spherical nucleus surrounded by carbon net-works with a bending structure. It shows that the outer shell,which is composed of graphitic crystallites, is of a rigid structure,while the inner core is chemically and structurally less stable dueto the thermodynamic instability of its structure. Arrangements ofcrystallites which contain inner/outer shells and fine particles incollected soot (with different sizes observed under HRTEM analy-sis) are shown in Fig. 1c.

In summary, the formation of soot, i.e. the conversion of HCfuel molecules into carbonaceous agglomerates, is an extre-mely complicated process. It is a kind of gaseous-solid phasetransition where the solid phase exhibits no unique physicaland chemical structures, and the transition occurs throughvarious chemical reaction and physical interaction steps. Anumber of approaches to soot modeling exist, but there is atrade-off between the capability of predicting the details ofsoot formation and computational time. Another issue in sootformation modeling is the complexity of simultaneous che-mical and physical phenomena, such as precursor formationfrom the gas phase chemistry, primary particle inception,nucleation, particle growth, coagulation and particle oxida-tion which are hard to describe in a series of mathematicalformula. So, simplified soot models that can produce morerealistic results in reduced computational time are highlydesired for engine design and emission control.5 Particulate matter.

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3. Soot formation

The formation of soot is a complex process, an evolution ofmatter in which a number of molecules undergo many chemicaland physical reactions within a few milliseconds. It is still not

clearly understood how soot particles and their precursors areformed despite the broad and extensive studies published in theliterature [16,17,24,28]. Many details of soot formation chemistryremain unanswered and controversial, but there have been a fewagreements which are summarized here [18]:

– Soot begins with some precursors or building blocks.– Nucleation of heavy molecules occurs to form particles.– Surface growth of a particle proceeds by adsorption of gas

phase molecules.– Coagulation happens via reactive particle–particle collisions.– Oxidation of the molecules and soot particles reduces soot

formation.

Many references proposed various soot formation processes inwhich many of them have in common. These widely agreedmechanisms proceed in three steps and are depicted in Fig. 2.Large aromatic rings are formed mainly through addition of lightHCs (acetylene) molecules in the molecular scale. Primary sootparticles are supposed to be formed either by surface growth orcoagulation of these larger aromatic compounds.

Tree and Svensson [25] presented five steps of soot formationas depicted schematically in Fig. 3. In which, acetylene and PAHmolecules are involved during precursor formation after fuelpyrolysis. Nucleation, surface growth, coalescence and agglom-eration are considered as afterwards steps.

According to the Tree and Svensson [25] soot formationmechanism, the HC fuel is degraded into small HC separateradicals. Later on HC radicals are added for growth of unsatu-rated HCs when they contain a sufficiently large number ofcarbon atoms in their structure. An increase in the acetyleneconcentration acetylene mainly helps the formation of largeraromatic rings. The growth is supposed to happen by coagula-tion of larger aromatic structures forming primary soot parti-cles. These primary particles quickly coagulate, simultaneouslypicking up molecules from the gas phase for surface growth.Five common steps in soot formation are briefly discussed innext sub-sections.

3.1. Precursors for soot formation

The species that are considered to be the onset for sootformation and growth are referred to as precursors. Soot inceptionis a mechanism through which the precursors are resulted fromfuel combustion to form soot particles. Inception is poorly und-erstood because the nascent soot particles are extremely small(about 1 nm in diameter) thus making experimental investigationsvery difficult [18]. Among them acetylene has received great att-ention [30]. Acetylene has been identified by Glassman [24] andlater confirmed by Richter and Howard [18] as a very imp-ortant precursor for soot formation in diesel combustion, mostlikely because the first aromatic rings are formed from C2 and C3additions [31].

In 1990s, Frenklach and Wang [32] proposed that the addi-tion of acetylenes lead to the formation of first aromatic rings,and those aromatic rings are the soot precursors. Due to limitedformation of some intermediate molecules via acetylene andcomplexity of experimental studies, PAHs molecules instead of

Fig. 1. (a) SEM image of soot aggregates in diesel exhaust collected from a ToledoArea Regional Transit Authority (TARTA) bus (b) Substructure of a soot particle (c1)Microstructure of diesel soot particles [26] (c2) HRTEM image of collected sootfrom a combustion chamber.

Fig. 2. A conceptual description of progression of soot formation in three steps [17].

Fig. 3. Schematic diagram of the soot formation step process from gas phase to solid agglomerated particles in five steps [25].

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acetylene are much more considered as soot precursor in dieselcombustion [33,34]. PAH formation and its growth appear todepend mainly on the type of fuel. Some of the reaction seq-uences which depict the formation of first aromatic rings aresummarized elsewhere [26].

3.2. Nucleation

The next step, nucleation or inception of particles from heavyPAH molecules, bridges the transition from gaseous media in acombustion process to heavy molecules that eventually turn intonascent soot. The molecular mass of nascent soot is approximately2000 atomic mass unit (amu) [16] with an effective diameter ofabout 1.5 nm (can be detected by HRTEM) [18], while it iscommonly believed that nucleation starts at lower amu around300–700 [35].

3.3. Mass growth

Soot surface growth is the overall mechanism through whichsoot particle masses grow via the addition of gas species such asacetylene and PAH molecules/radicals. There is no clear distinctionbetween the end of the nucleation and the beginning of surfacegrowth and in reality the two processes are concurrent. Frenklach[35,36] introduced the surface growth reaction mechanism back in1980s. Soot particles undergo surface reactions with gaseousspecies via the hydrogen abstraction carbon addition (HACA)process [37,38]. For HACA growth, the soot surface property isan important factor in soot mass growth. C–H bonds on the surfaceof the soot interfere with H and OH radicals to form reactive sites,where gaseous molecules (particularly acetylene) can be added tothe surface of the soot particle [37,39].

3.4. Coagulation

As depicted in Figs. 2 and 3, during nucleation, particlegrowth happens through the coagulation step, i.e., a combina-tion of two or more particles to form a larger particle, some-times called coalescence [17,25]. The results of experimentsdepict that particle coagulation process occurs almost immedi-ately after the soot particle formation, or when soot particles arerelatively small or young [17]. Sticking collisions betweenparticles during the mass growth process significantly increasethe particle size and decrease the number of particles withoutchanging the total mass of soot present. Sometimes individualor primary particles stick together to form large groups ofprimary particles which maintain their shape. In this case theprocess is called agglomeration. So, the coagulation processforms a large particle by combining small particles, where du-ring agglomeration the primary particles stick to each other,forming a group of chain-like aggregates. An example of agg-lomeration is easily found in the collection of exhaust soot froma diesel engine. In soot exhaust, soot consists of primaryparticles which are spherical in shape, and they are agglomer-ated to form long chain-like structures as shown in Fig. 1a.

3.5. Oxidation process

Soot oxidation is the result of the processes that reduce themass of soot by converting the solid soot particles or part of themback into gases (e.g. CO and CO2). Oxidation is similar to thesurface growth in a sense that the surface area of the particlesaffects the rate of oxidation. Oxidation takes place on the surfacesof soot particles and decreases the mass of soot and reduces themass of carbon accumulated in the soot particles [17]. Unlike thesurface growth of soot, which occurs in a specific step, oxidation

happens all the time during and after soot formation. Oxidizingelements are O, O2, and OH under fuel-rich conditions, but in fuel-lean media H2O, CO2, NO, N2O, and NO2 are also possible oxidants[19]. More oxidation models in which oxidants other than O2 areinvolved are presented elsewhere [19,40].

A comprehensive review for the fundamentals of soot forma-tion mechanism is beyond the scope of this review. More in-depthreviews were provided by earlier studies [16,17,24,28].

4. Modeling

Soot mechanism is difficult to be mathematically modeledbecause of the large number of primary components of dieselfuel, quite complex combustion mechanisms, and the heteroge-neous interactions during soot formation [41].

Soot models are broadly categorized into three subgroups[42]. Empirical (equations that are adjusted to match experi-mental soot profiles), semi-empirical (combined mathematicalequations and some empirical models which used for particlenumber density and soot volume and mass fraction), anddetailed theoretical mechanisms (covers detailed chemicalkinetics and physical models in all phases) are usually availablein literatures for soot models.

Empirical models use correlations of experimental data topredict trends in soot production [43–45]. Empirical models areeasy to implement and provide excellent correlations for a givenset of operating conditions. However, empirical models cannot beused to investigate the underlying mechanisms of soot production.So, these models are not flexible enough to handle changes inoperating conditions. They are only useful for testing previouslyestablished designed experiments under specific conditions.

Second, semi-empirical models solve rate equations that arecalibrated using experimental data [43,46,47]. Semi-empiricalmodels reduce computational costs primarily by simplifying thechemistry in soot formation and oxidation. Semi-empirical modelsreduce the size of chemical mechanisms and use simpler mole-cules, such as acetylene as precursors.

Detailed theoretical models use extensive chemical mechan-isms containing hundreds of chemical reactions in order to predictconcentrations of soot. Detailed theoretical soot models contain allthe components present in the soot formation with a high level ofdetailed chemical and physical processes.

Such comprehensive models (detailed models) usually takehigh financial burden for programing and operating, and muchcomputational time to produce a converged solution. On theother hand, empirical and semi-empirical models ignore someof the details in order to make complex model simple and toreduce the computational cost and time. Thanks to recenttechnological progress in computation, it becomes more feasibleto use detailed theoretical models and obtain more realisticresults. However, further advancement of comprehensive theo-retical models must be preceded by the more detailed andaccurate formation mechanisms.

On the other hand, models that are based on a phenomenolo-gical description have found wide use recently. Phenomenologicalsoot models, which may be categorized as semi-empirical models,correlate empirically observed phenomena in a way that is con-sistent with the fundamental theory, but is not directly derivedfrom the theory. Phenomenological models use sub-models devel-oped to describe the different processes (or phenomena) observedduring the combustion process. These sub-models can be empiri-cally developed from observation or by using basic physical andchemical relations. Advantages of phenomenological models arethat they are quite reliable and yet not so complicated. So, they areuseful, especially when the accuracy of the model parameters is

H. Omidvarborna et al. / Renewable and Sustainable Energy Reviews 48 (2015) 635–647638

low. For example, as presented by Argachoy and Pimenta [48], thephenomenological models can predict the soot formation evenwhen several operating conditions are changed in a system andthe accuracy cannot be guaranteed. Examples of sub-models ofphonological empirical models could be listed as spray model, lift-off model, heat release model, ignition delay model, etc. [47,48].

4.1. Empirical and semi-empirical soot models

This section presents diesel soot models published mainly onempirical and semi-empirical approaches. Several models pro-posed in these categories consider two competing reactions, sootformation and soot oxidation, as a two-step approach [40,47,49].Both formation and oxidation rates are highly temperature depen-dent, and they are represented by Arrhenius type expressions. Sootformation rates are proportional to fuel vapor pressure whileformation expressions contain no dependence on the type, com-position or structure of fuel. Also, the two-step models contain noinformation on particle size or agglomeration of soot, both ofwhich affect the surface area of soot available for a given mass ofsoot produced as explained in Section 3. The oxidation expressionincludes only O2, leaving out other important oxidation mechan-isms such as OH reactions.

A study on soot formation by Tesner et al. in 1971 [46] was oneof the first soot models in these categories that include abranched-chain process and soot particle formation. Tesner etal.'s model implemented an idea that soot is formed as a result ofadsorption of radical nuclei on the precursor surface. In the sameyear, an empirical model was proposed by Khan et al. [44,50]. Thismodel predicts soot emissions from diesel engines based on theassumption that the rate of soot production is entirely a functionof soot nucleation rate. It means that the rates of particle growthand oxidation were neglected in this model. Also, the modelassumes that the diameter of a soot particle is not a function ofengine operating conditions at different speeds or loads, which isregarded as unrealistic. The model includes some modeling para-meters determined by comparing the output of the model withexperimental data [42,44].

A few years later, Hiroyasu et al. [49] proposed one of the mostwidely used models that are essentially based on empiricalformulas for predicting the formation and oxidation of sootparticles. They found that their two-step soot model was primarilyaffected by pressure, temperature, and equivalence ratio, which isthe actual fuel to oxidant ratio normalized by the stoichiometricfuel to oxidant ratio. The model includes a soot formation andoxidation rate, which incorporates the available fuel mass andoxygen partial pressure. Eq. (1) calculates the first order rate of netsoot formation (dms/dt) using a combination of soot formation rateand soot oxidation rate for Hiroyasu's model. Then, the sootformation rate equation (Eq. (2)) follows an Arrhenius-type rela-tion with the mass of vaporized fuel (mfg). Oxidation rate of soot(dmsc/dt) in Eq. (3) also follow an Arrhenius-type equation as afunction of mass of soot in the system (ms) and oxygen pressureðPo2 Þ. The oxidation rate of soot is almost a second order (1.8) ofpressure whereas the formation rate of soot is a half order ofpressure

dms

dt¼ dmsf

dt�dmsc

dtð1Þ

dmsf

dt¼ AfmfgP

0:5e �Esf =RTð Þ ð2Þ

dmsc

dt¼ Acms

Po2

PP1:8eð�Esc=RTÞ ð3Þ

where msf is the mass of the formed soot, and mfc is the mass ofoxidized soot. Esf and Esc are activation energies of soot formation

and soot oxidation, respectively. Af and Ac were determined bymatching the calculated soot and experimental soot in theexhaust. The Hiroyasu's model has been very helpful in providingknowledge on the bulk distribution and transport of the soot inthe high-temperature combustion environments of conventionaldiesel engines [51]. Moreover, owing to ease of implementationinto computational fluid dynamic (CFD) codes, this model and itsmodifications have acquired wide popularity in the communityengaged in multidimensional diesel combustion simulations [52].

The two-step approach of Hiroyasu's model [49] is regardedoversimplified for the diesel soot formation processes becausethey proposed a two-step empirical soot model for predicting theformation and oxidation of soot particles and the model under-predicted the peak in-cylinder soot concentration [51]. Sootformation formula of Hiroyasu's model contains no dependenceon the type, composition or structure of fuel. The oxidationexpression includes only oxygen molecules in the model [53].Hiroyasu's model is regarded very practical and simple, but itneeds more parameters to be upgraded for further studies [54–56].

Another basic semi-empirical model developed for dieselengines was proposed by Nishida et al. [57,58], Belardini et al.[59], and Patterson et al. [51]. In 1993, Gorokhovski et al. [60]assumed that soot is generated from a stable HC intermediatespecies. It means that the soot surface growth rate was determinedby experimental data on a final soot volume.

The Hiroyasu-Nagle and Strickland (HNS) soot model has beenanother very popular two-step semi-empirical model for sootformation in diesel engines [47,61]. The rate of soot formationand oxidation were expressed again in an Arrhenius-type equationas follows:

Rate of formation¼ Af P0:5e�Ef =RT ð4Þ

Rate of oxidation¼ XO2AoP1:8e�Eo=RT ð5Þ

where Af and Ao are the pre-exponential factors; Ef and Eo are theactivation energies; XO2 is oxygen molar fraction; R is the ideal gasconstant; and T is the gas temperature. Hiroyasu's approach isadopted to describe soot formation while its oxidation is estimatedby the Nagle-Strickland and Constable model [54,55].

Moss [62] also presented a semi-empirical two-step modelwhich is slightly different from the previous models. In this model,not only nucleation and oxidation rates, but also coagulation andgrowth rates, were considered and implemented in the process ofsoot formation. The superiority of Moss's model is not surprisingsince it simulates the processes of nucleation, surface growth, andcoagulation, whereas other two-step models, such as Khan et al.[50], rely on a simple kinetic expression for soot nucleation rates.

Lindstedt outlined reaction steps for the formation and growthof soot particles [63]. Detailed gas phase chemistry and simplifiedsteps of nucleation, surface growth and particle agglomerationwere incorporated in the model. The soot nucleation and surfacegrowth reactions are linked to the gas phase chemistry. Lindstedtpaid much attention to the problems in modeling the soot massgrowth. Lindstedt considered four models for the soot growthreactions [42]. More details on the recent soot models areprovided later in Section 4.2 that addresses detailed mechanismsand phenomenological soot models as well.

4.2. Recent studies on soot modeling with emphasis onphenomenological studies

Soot formation phenomenon is far from being fully understoodtoday and models available for simulation of soot in combustiondevices remain of relatively limited success, despite significantprogresses made over the last decade. Since Hiroyasu et al. [47,64]

H. Omidvarborna et al. / Renewable and Sustainable Energy Reviews 48 (2015) 635–647 639

and Khan et al. [50,65] presented ones of the earliest models forthe prediction of soot production from a diesel engine, variety ofsoot models with different levels of complexity have been pro-posed and applied to soot formulation.

As mentioned earlier soot formation and oxidation are verycomplex, and it is not possible to exactly model a complex processonly with simplified models. Unlike the empirical and semi-empirical models which extremely simplify the soot formationprocess, the detailed model describes the formation, growth, andoxidation of soot with a detailed chemical reaction mechanism.The extremely high demand of computing time of detailed sootmodels makes them unrealistic for simulation of diesel enginecombustion. Hence, most of the investigations conducted in a realconfiguration such as multidimensional diesel engines utilizecoarse modeling schemes to take advantage of easy implementa-tion and low computational cost. This section reviews the pub-lished papers that focused on the detailed models and those withthe emphasis on phenomenological methods.

With recent advances in computer technology and devel-opments in mathematical sub-models, it is now possible toobtain useful predictions and visualizations of complex sys-tems. Therefore, numerical simulation of such complex systemsis considered in many areas. In soot modeling, numericalsimulations can also be divided into two main classes: phe-nomenological modeling and multidimensional CFD modeling.In phenomenological modeling, the spatial variations are oftensimplified by zero-dimensional or one-dimensional models,while multidimensional modeling is designed to take intoaccount all the spatial variations of reactive fluid flow in dieselengines simultaneously [26]. In order to improve the accuracyand predictability, phenomenological multi-step soot modelshave been implemented in many empirical, semi-empirical anddetailed models. In other words, phenomenological modelsdescribe the complex process of soot formation and oxidationin terms of several global steps that are particularly advanta-geous for practical combustion simulations.

In 1994, Fusco et al. [66] proposed a phenomenologicalsoot model to overcome some limitations of the previous sootmodels for combustion conditions of a diesel engine. Themodel accounts for the number of carbon atoms of the majorconstituent molecules in the fuel and incorporates the physi-cal process of inception, surface growth, coagulation andoxidation into the eight-step phenomenological soot model.They also compared their model with the existing two-stepempirical models and criticized the non-applicability of thetwo-step empirical models for a wide range of operationconditions in diesel engines. The model consists of fourdifferential equations balancing between the rates of particlenumber change, soot precursor radicals, acetylene and sootvolume fraction. Just like the previous formulations, Arrh-enius-type rate expression has been used for most of the

processes except for coagulation and oxidation steps. Thesurface growth species, which was assumed to be acetylene,enhances the mass of the soot particles. Schematic diagram ofthe phenomenological model is presented in Figs. 4 and 5.

Fig. 4 shows that the portion of the fuel is converted to sootprecursor radicals (R1) and growth species (C2H2 by R2). Radi-cals and growth species are considered to be separated spe-cies, although they could be the same species at least at thebeginning of the soot formation process. A portion of theprecursor radicals are oxidized (R3) and the rest are convertedto soot particles (R5). The growth species increase the mass ofsoot particles by R6. Oxidation is assumed that it does notaffect the particle number density. Growth species (C2H2) maydisappear, and the mass of soot particles may decrease viaoxidation (R4 and R7), respectively. The number of sootparticles can decrease due to coagulation with other sootparticles (R8). Their modeling result demonstrated that moresoot is produced when more acetylene is available. This resultmeans that the final amount of soot depends on the balancebetween soot formation and oxidation in the both solid andgas phases.

In 1998, a modified version of the phenomenological modelof soot formation by Kazakov and Foster [43] has been imple-mented into the model developed by Fusco et al. [66]. The modelincludes major generic processes involved in soot formationduring combustion; formation of soot precursors, soot particlenucleation, coagulation, surface growth, and oxidation. AfterKazakov and Foster, Fusco's original model [66] extended by Liuet al. [67] to produce a nine-step model as presented below:

1. Acetylene formation from fuel pyrolysis.2. Soot precursor formation from acetylene.3. Particle inception from soot precursors.4. Soot particle coagulation.5. Surface growth from acetylene.6. Oxidation by O2.7. Oxidation by OH.8. Acetylene oxidation by O2.9. Precursor oxidation by O2.

The phenomenological model covers oxidation of precursor(acetylene) and fuel by either O2 or OH. Also, the role of acetylenein inception and surface growth was very important in Liu et al. to

Oxidation

FuelPyrolysis

Soot precursor radicals

Growth species (acetylene)

Oxidation

Inert products

Inert products

(1)

(2)

(3)

(4)

(5)

(6)

Inception

Surface growth

Soot particles

Inert products

Soot particles

Oxidation

Coagulation

(7)

(8)

Fig. 4. Schematic diagram of eight-step phenomenological soot model presentedby Fusco et al. [66].

Fig. 5. Nine steps in soot modeling presented by Tao et al. [72].

H. Omidvarborna et al. / Renewable and Sustainable Energy Reviews 48 (2015) 635–647640

develop a nine-step model [67]. The nine-step model had a funda-mental weakness which is still unable to express the role of fuelcomposition and structure whereas each of the acetylene forma-tion rates is reported to be dependent on fuel structure [25].

Conventional diesel fuel contains around 30–35% PAHs, andnumerous studies in recent years have demonstrated that aro-matics such as PAHs play a key role in the soot formation processin diesel engines [68,69]. Idicheria and Pickett [69] focused on therole of PAH in soot formation in diesel combustion. They came tothe conclusion that PAH chemistry might play an important roleboth for accurate prediction of soot mass and distribution. There-fore, the chemical kinetic mechanisms of the mixture of n-heptaneand aromatics (to present as a diesel surrogate) included in recentmodeling studies are considered more accurate to simulate sootformation than previous models. Since it is not practically possibleto have kinetic reaction mechanisms for all of the hundreds oreven thousands of species present in conventional diesel fuel,n-heptane is used in many modeling studies of diesel combustionas a diesel surrogate [30,70,71]. N-heptane has a cetane rating of56 that is typical of ordinary diesel fuels, and that is why thecombustion process in diesel engines has often been simulatedusing n-heptane as a surrogate diesel fuel [71].

In Tao et al.'s [70] model, diesel fuel is assumed to be single-component, and its oxidation chemistry is represented only by then-heptane kinetics. The chemical mechanism simplified to a size of65 species and 273 elementary reactions. Here formation reactionsof PAHs (up to few aromatic rings) from acetylene were consideredas initiator for soot formation in soot modeling.

In 2002, Frenklach assumed the formation and growth ofPAHs as the first step in soot formation [37]. In this model, theparticles grow via surface reactions similar to the growth rea-ctions for PAHs, i.e., mainly by the HACA mechanism. Whenparticles collide with each other, either they form new sphe-rical particles or agglomerates. In that paper, the discussionshifted from phenomenological possibilities to specifics of rea-ction pathways.

In a parallel study with Liu et al. [67], nine-step phenomen-ological soot model was updated for predicting soot formation andoxidation processes in diesel engines by Tao et al. [72]. The brandnew model presented by Tao and coworkers consist of two parts,detailed chemical reaction mechanism and a phenomenologicalsemi-empirical soot model.

Tao's model retains the main features of his original model[43,66], but contains three major modifications: (1) fuel pyr-olysis leads solely to acetylene formation; (2) the soot pre-cursor is formed merely via acetylene (i.e., not directly fromfuel); (3) an OH-related soot oxidation step is added. In theearlier study of Liu et al. [67], the OH concentrations werecalculated using the concept of chemical equilibrium, theassumption of which was unfortunately unrealistic whenapplied to transient diesel combustion processes. The updatednine-step soot model [72] was successfully applied to analyzethe soot distribution structure in a conventional diesel for abenchmark heavy-duty diesel engine (Cummins) based onwhich a comparison to the two-step soot model was atte-mpted.

In 2010, Vishwanathana and Reitz [73] presented a practicalmodel framework only based on four fundamental steps: sootinception through a four-ring PAH species, surface growth ofacetylene, coagulation of acetylene to form soot, and soot oxida-tion via oxygen and OH. They concluded that the soot model isfairly sensitive to the PAH chemical mechanism [74].

Simultaneously, Cheng et al. [75] presented an improveddetailed soot model for the numerical investigation of sootformation, mass concentration, and size distribution in dieselengines. The effects of soot precursors, including isomers of

acetylene and PAHs, and the physical processes of PAH depositionon the particle surface, soot formation, and particle surface growthwere considered into the model. They found that large amounts ofsmall-size soot particles (in the range of 5–40 nm) were producedat the initial stage of combustion by the pyrolysis reactions andpolymerization of the HC fuel. In the intermediate stage ofcombustion, soot particles continued to grow by particle coagula-tion, surface growth, and the deposition of PAHs. In the final stageof combustion, the particle size distribution stabilized in the rangeof 5 to 20 nm due to the influence of further oxidation reactions.

Jia et al. [76] quantitatively validated and improved thephenomenological soot model over wide operating conditionsof homogeneous charge compression ignition (HCCI) combus-tion. The phenomenological model developed in this researchas summarized in Table 1 is based on the work of Tao et al.[77]. By comparing experimental results, necessary improve-ments have been made to the model for describing the sootformation process under various conditions. The complexprocesses of soot formation and oxidation are divided intoseveral steps including acetylene (C2H2) formation from pyr-olytic decomposition of fuel, precursor formation via C2H2

conversion, particle inception from precursor, particle surfacegrowth by C2H2, particle coagulation, particle surface oxida-tion via oxygen (O2) and OH. Later, a six-step phenomenolo-gical soot model with particle dynamics was developed byPang et al. [78]. The sub-model for soot formation was con-structed based on Jia's soot model [104] by introducingnecessary improvements and optimizations. The schematicrepresentation in Fig. 6 shows the structure of the six-stepphenomenological soot model developed in their study. Sootformation and oxidation process are divided into several stepsincluding soot precursor formation via C2H2, A3 (aromaticstructure with 3 rings) and A4 conversion, particle inceptionfrom soot precursor, particle surface growth by C2H2 and A1,particle coagulation, particle surface oxidation via O2 and OH,and precursor oxidation. The new model retains the mainfeatures of the original one [76] but two major modificationsare as follows:

1. PAHs (A3, A4) are used as precursor species.2. Particle surface growth by A1 is added in the new soot model.

In Fig. 6, carbon atoms for soot precursor and soot particleare represented by C(PR) and C(S), respectively. The rates ofreactions, including soot precursor formation (RS(1), RS(2), RS(3)), particle inception (RS(4)), particle surface growth (RS(5),RS(6)), and soot precursor oxidation (RS(10)) were assumed tobe in the form of Arrhenius equation. In this approach, thisphenomenological soot model has gained significant improve-ments in performance by incorporating the PAH chemistry intothe model. A modified skeletal PAH mechanism for the phe-nomenological soot formation was integrated into a primaryreference fuel (PRF) oxidation mechanism where A3 and A4were the soot precursor species. The new skeletal PAH mechan-ism is capable of describing the formation process of PAHsbeyond A1 and up to A4.

As pointed out by Vishwanathan and Reitz [73] and Ra andReitz [79], the PRF mechanism developed earlier by Ra and Reitz[80] under-predicts the concentration of C2H2, which is consideredas an important species in soot inception and growth processes. Ifa PAHs mechanism is applied, C2H2 is also an important speciesthat affects the PAH growth process through the well-knownHACA growth mechanism [81]. Table 1 summarizes the recentmodeling approaches and their results that are presented inthis paper.

H. Omidvarborna et al. / Renewable and Sustainable Energy Reviews 48 (2015) 635–647 641

Table 1Concise description of works done on soot modeling mainly for diesel engines.

Author name(year)

Model specification Results/notes

Belardini et al.[41]

N-heptane is selected as a diesel surrogate. The model over-predicts both acetylene and soot volume fraction.Assuming acetylene as the crucial pyrolitic species. Better results can be obtained by model's constant optimization.

Frenklach et al.[82,83]

The model combines recent developments in gas-phase reactions, aromaticchemistry, soot particle coagulation, soot particle aggregation, and developsa new sub-model for soot surface growth.

The surface growth and oxidation of soot particles are describedconsistently with the kinetics of gaseous PAHs.

Daly and Nag[84]

A new gas phase kinetic model using Westbrook's gas phase n-heptanemodel and Frenklach's soot model [82,83].

It represents the state of the art in detailed soot modeling for dieselcombustion.

614 Species and 2883 reactions are involved in the complex reactionmechanism.

Tao et al. [70] Diesel fuel is assumed to be single-component, and its oxidation chemistryis represented by the n-heptane kinetics.

Molecular precursors of soot produced during the rich burning of thesprays contribute to soot formation.

The chemical mechanism reduced to a size of 65 species and 273elementary reactions.

Kong et al. [30] A reaction mechanism of n-heptane is coupled with a reduced NOx

mechanism to simulate diesel fuel oxidation and NOx formation.Both experiments and models reveal that soot emissions peak when thestart of injection (SOI) occurs.

The soot emission process is simulated by a phenomenological sootmodel that uses a competing formation and oxidation rate formulation.Acetylene is selected as a soot precursor.

Boulanger et al.[85]

A phenomenological three-equation soot model in diesel enginecombustion.

Some distinct features of this new soot model are:� No soot is formed at low temperature.� Minimal model parameter adjustment for application to different fuels.� There is no need to prescribe the soot particle size.

Vishwanathanand Reitz[34]

A reduced n-heptane chemistry mechanism has been extended to includePAH species up to four fused aromatic rings (pyrene).

Soot formation and growth regions are not adequately represented byusing acetylene alone as the soot inception species.A simpler model that only considers up to phenanthrene (A3) as the sootinception species has good possibilities for better soot locationpredictions.

Various soot inception species have been tested.

Tao et al. [72] Nine-step phenomenological soot model. Nine-step model is not only computationally efficient but alsofundamentally sound.Model includes a detailed chemical reaction mechanism and a

phenomenological semi-empirical soot model. After calibration, the model is suitable to be integrated with geneticalgorithms for system optimization over a controllable range ofoperations.

Mosbach et al.[86]

A detailed model for the formation of soot in internal combustion enginesdescribing not only bulk quantities such as soot mass, number density,volume fraction, and surface area but also the morphology and chemicalcomposition of soot aggregates.

A detailed chemical kinetic mechanism describing the combustion ofPRFs is extended to include small PAHs such as pyrene, which function assoot precursor species for particle inception in the soot model.

Jia et al. [76]) An improved phenomenological soot model coupled with a reducedn-heptane chemical to describe soot formation and oxidation processes inHCCI combustion.

Even with a detailed chemical mechanism, soot formation and oxidationstill remain as challenges.

The phenomenological soot model coupled with reduced fuel chemicalmechanism showed satisfactory agreement with the experiments.

Vishwanathanaand Reitz[73,74]

Reduced n-heptane and PAH chemistry mechanisms are formulated fromthe literature.

The model is based on four fundamental steps: soot inception through afour-ring PAH species, surface growth through acetylene, sootcoagulation, and oxygen- and OH-induced soot oxidation.Acetylene was selected as a soot precursor.

Sukumaranet al. [87]

A multistep soot model coupled with reaction mechanisms for fueloxidation and PAH formation.

Soot emissions from the engine are highly sensitive to local temperatureand chemical compositions.

N-heptane mechanism is combined with a detailed PAH mechanism, bychoosing pyrene as precursor.The overall reaction mechanism consists of 68 species and 145 reactionsand is used with a multistep soot model.

Pang et al. [78] 12 Species and 26 reactions for the formation of PAH are integrated into askeletal mechanism for the oxidation of PRF (n-heptane and iso-octane).

The results prove a very good agreement with experimental data and thenecessity of including PAHs chemistry for soot modeling.

Six-step phenomenological soot model with PAHs (A3 and A4) as aprecursor.Particle surface growth by A1 is added in the new soot model.

Naik et al. [88] N-hexadecane, heptamethylnonane, 1-methylnaphthalene, and decalinare used to represent standard European diesel.

A new pseudo-gas soot model coupled with the fuel chemistry tosimulate an in-cylinder soot nucleation, growth, and oxidation processes.

A validated detailed surrogate mechanism containing 392 species and2579 reactions was employed to model the chemistry of fuel combustionand emissions.Analyses are conducted under low-temperature combustion (LTC)condition.

Cheng et al. [75] An improved soot model coupled with a detailed mechanism of reduceddiesel surrogate fuel (n-heptane / toluene).

The particle emissions increase with increasing engine load.

Chemical kinetic mechanism contains 70 species and 313 reactions. Particle concentration and average particle size significantly increase atthe starting stage of the combustion process and quickly stabilize.Isomers of acetylene and PAHs are selected as precursors.

H. Omidvarborna et al. / Renewable and Sustainable Energy Reviews 48 (2015) 635–647642

4.3. Soot formation mechanism from oxygenated fuels

Investigations of alternative fuels for internal combustionengines have recently become important due to the growingconcerns about the future availability of oil reserves andenvironmental sustainability. Among them, biofuel is receivingincreasing public and scientific attention especially from thetransportation sector driven by its carbon neutrality and com-parability with existing engines [13,14,20,27,89,90].

Several investigators have concluded that the structure ofoxygenated fuel has an effect on the amount of soot reductionpossibly achieved with a given amount of the oxygen atomsincluded in the fuel structure [13,91,92,93]. The oxygenatedfuels such as BD usually consist of fatty acid methyl esters(FAMEs)/fatty acid ethyl esters (FAEEs), and they should beconsidered for their reactions in soot modeling. FAMEs andFAEEs are produced through the transesterification process ofvegetal oils or animal fat with methanol or ethanol as a catalyst.FAME has lower energy content than diesel due to its highoxygen/low carbon contents, and as a result, its combustion andfuel consumption can be affected accordingly. The main fattyacids in rapeseed and soybean oils are oleic (C18:1 monounsa-turated) and linoleic (C18:2 polyunsaturated) acids [94].

Compared to regular diesel, the oxygenated structure of BDenhances oxidation process in soot formation and dramaticallyreduces the mass of soot. On the other hand, kinetic studies of BDsurrogates showed that early CO2 production from the methylester (ME) group in methyl decanoate (MD) has important impactson ignition and soot production, because if the oxygen in the fuelimmediately produces CO2, it becomes less effective in reducingsoot production [95]. Therefore, it is necessary to develop wellvalidated models for the combustion and the oxidation of theoxygenated components of BD to account for the effect of extraoxygen on soot formation. Among the emission studies conductedon oxygenated fuels, Miyamoto et al. [91] concluded that sootreduction was related to the oxygen content of the fuel and not tothe type of fuel. Also, the study of Song et al. [93] on sootemissions from oxygenated fuels showed that the operatingconditions of a diesel engine would change the morphology ofsoot and it would produce smaller particles in idle mode whileusing oxygenated fuel.

Not many papers have been published on soot formationmodeling for oxygenated fuels such as BD compared to regulardiesel modeling [96–98]. Although the effect of oxygen contenton soot formation and oxidation process are considered highly

important in soot generation from BD combustion, it is alsoexpected that the widely differing physical properties of BDfrom regular diesel will influence the combustion mechanismand the related emissions formation. But, a basic understandingfrom the kinetic modeling of oxygenated molecules presented inthese studies may provide clues for soot reduction processes inBD combustion.

BD surrogates are mixtures of one or more simple fuelcomponents that are designated to emulate physical and che-mical properties of BD. While surrogate mixtures can demon-strate more than one characteristic of the fuel to be simulated,more often than not many other components are required toemulate the wide variety of properties that are of interest toresearchers. As mentioned earlier n-heptane is normally usedfor the diesel surrogate while usually M9D (methyl 9 decenoate),n-butanol, and MB (even if the small size of these moleculesprevents them from the combustion chemistry of the largemolecules present in BD) are extensively used for BD [96,99].As mentioned above, unsaturated esters are the most abundantesters in BD, but very few studies have been dedicated tocombustion modeling for unsaturated esters [100].

As stated above, the oxygen content is an important factor to beinvestigated in BD study because the oxygen content in fuel notonly provides more oxygen to burn carbon, but also displaces andreduces the amount of carbon that needs to be burned. In addition,

Fig. 6. Schematic representation of the improved phenomenological soot model [78].

107

108

109

105

105

91

110

110

Fig. 7. Reduction of PM, smoke, or integrated jet-soot as a function of oxygenweight percent in the fuel from numerous experiments reported in the literature[25,91,105,107–110].

H. Omidvarborna et al. / Renewable and Sustainable Energy Reviews 48 (2015) 635–647 643

considering the fact that PAH is a main cause for soot formation,BD has an advantage of low PAH formation as BD was observed toemit lower PAH emissions than normal diesel [101]. It wasreported that potential for soot precursor formation disappearsalmost completely at an oxygen-fuel ratio of 25 wt% [102], anddrops to an insignificant level with extra 30–40 wt% oxygen in afuel [103]. It means that the BD's molecular structure and itsoxygen content should be the main factors for soot precursorformation. A summary listing of references and fuels including BDis presented by Tree and Svensson [25] (Fig. 7), which is sufficientto draw several conclusions on the effects of oxygen contents onsoot formation. It should be recognized that the results aregathered from the in-cylinder soot data while others are obtainedfrom the exhaust emission data. Some of the findings are asfollows:

1) Soot emissions are reduced by increasing fuel oxygen contents,based on Smith's study [104]. The reason would be because ofoxidization of acetylene to relatively inert products in thepresence of enough O2 and OH.

2) More than one study has demonstrated a complete or nearcomplete elimination of soot when fuel oxygen content reaches30% or more.

3) Scattered data points indicate that mass fraction of oxygenatedfuel can have various amounts of percent PM reduction.

4) Miyamoto et al. [91,105] and Nabi et al. [106] have reported anearly linear decrease in soot concentration as fuel oxygencontents increase. The linear relationship is very interestingbecause the required oxygen content to burn the fuel should berelated to molar quantities for oxygen and carbon, not the massof them.

According to Curran et al. [103], n-heptane was used as arepresentative surrogate of diesel fuel, and methanol, ethanol,dimethyl ether, dimethoxymethane and MB were used as oxyge-nated fuel additives to simulate the oxygenated contents. It wasfound that when the overall oxygen content in the fuel reachedapproximately 30–40% by mass, production of soot precursors fellapparently to zero. Later on Mueller and coworkers [111] exploredcharacteristics of soot and soot-precursor formation from oxyge-nated fuels (di-butyl maleate and tri-propylene glycol methylether). They defined four goals for their study:

1. To introduce the “oxygen ratio” for accurate quantification ofreactant-mixture stoichiometry for both oxygenated and non-oxygenated fuels.

2. To provide experimental results demonstrating that some oxy-genates are more effective at reducing diesel soot than others.

3. To present results of numerical simulations showing thatdetailed chemical-kinetic models without complex fluidmechanics can capture some of the observed trends in thesooting tendencies of different oxygenated fuels.

4. To provide further insight into the underlying mechanisms bywhich oxygenate structure and in-cylinder processes can affectsoot formation in diesel engines.

In 2013, a simplified chemical reaction mechanism was deve-loped for modeling the combustion process and soot emissionsfor both non-oxygenated and oxygenated HC fuels by Wanget al. [96]. The final mechanism consists of 76 species and 349reactions [96]. They reported that soot emission can be greatlyreduced by addition of n-butanol. By blending n-butanol into anon-oxygenated HC fuel, air entrainment is enhanced by redu-cing the overall fuel to air ratio by introducing extra availableoxygen atoms through the n-butanol molecule. The predicted

soot emissions under various conditions agree quite well withthe experimental results.

Inspection of the published results leads to the conclusions asfollows [25]:

� There is a reduction in soot emissions with increased fueloxygen in all cases.

� More than one investigator has demonstrated a complete ornear complete elimination of soot when the fuel oxygencontent reaches 27–35%.

� The scattering of experimental data verifies that a given fueloxygen mass fraction can have various levels of soot reduction.

� Some authors [91,105,106,107,110] had observed a nearly lineardecrease in soot concentration as oxygen contents increasewhile others [92,108] found decreasing slopes with decreasingbenefits for soot reduction when oxygen contents increased.

� PAHs are known as soot precursors in diesel fuels, whereas theyare detected at very low-concentrations in BD combustion[19,26,106,107].

� BD has an advantage of low soot formation as BD was observedto emit lower PAH emissions than diesel fuel. Oxygen contentplays an important role in lowering emissions of PAHs and itmakes BD reactions more complex than diesel fuels. Therefore,including the detailed precursor formation and oxidation reac-tion in the BD combustion helps better model the soot forma-tion mechanism.

The high oxygen content of BD makes extra oxygen available tofacilitate the combustion of fuel, especially in the areas of very richin fuel. It can have a favorable effect on less occurrence of pyrolysisand more enhancement of soot oxidation in comparison withregular diesel combustion. In addition, a wide diversity of feed-stock selections for BD necessitates the inclusion of the effect ofbasic components of BD on combustion in the soot formationmechanisms and modeling. Because the major fuel components ofBD from soybean oil, for example, are much different from the BDfrom tallow oil or waste cooking oil, and these feedstock-specificeffect should be included in the soot modeling of BD.

5. Conclusion

Fundamental concepts and models about soot mechanism indiesel and BD emission from combustion are examined in thisreview paper. Fuel combustion process is very complex, and theirdetailed mechanisms on soot are not quite well understood. Fromthe literature review, it can be observed that the emission of sootfrom the BD-fueled engine is less than ULSD. However, moreexperimental and theoretical studies are needed to describe thecomplicated process of soot mechanisms in BD combustion.

Soot formation and oxidation steps were incorporated inearly studies on soot mechanism. Later on, due to emergingnew computational systems along with modern experimentaland analytical tools, common steps in soot mechanism wereidentified. Following these steps results in more accurateresults compared to experimental data as described earlier.Among the proposed models for different combustion sys-tems, empirical and semi-empirical soot models are foundrelatively simple and practical for specific systems whereexperimental results have been implemented into the models.In recent years, phenomenological models have been found tobe more effective tool for simple and easy prediction of sootmechanism, but case by case adjustment of the implementedparameters may be needed. Detailed models were introducedas the most accurate and comprehensive models. Thesemodels require a great deal of computational time and cost.

H. Omidvarborna et al. / Renewable and Sustainable Energy Reviews 48 (2015) 635–647644

It is concluded that modeling of acetylene and its isomers as astarting component for formation of soot precursors seems tobe a reasonable approach to the diesel soot modeling. The sizeof the model is important in determining the amount ofcomputational time in detailed models, because the rate ofsoot precursor formation for each fuel will be dependent onfuel structure.

Regarding soot formation in BD combustion, molecules suchas MB, MD, n-heptane, and MS were identified as common fuelsurrogates in modeling. The degrees of oxygenation andsaturation (i.e., the number of double bonds) of BD fuels appearto be the important factors to be included in BD soot modeling.This is because of the number and position of double bondswhich may effect the reaction pathways and mechanisms. Forthe BD soot modeling, due to lower emission of PAHs, theoxidation of precursors such as PAHs may be excluded. On theother hand the effect of early CO2 production in BD combustionon soot formation should be considered.

Modeling of soot formation has to address all these aspects ofregular and BD combustions. In order to develop more robustand reliable models for soot mechanism, it is recommended thatmore reasonable assumptions be made based on a better under-standing of chemical and physical interactions in soot mechan-ism. A fundamental challenge in soot modeling for BD is theinability to predict differences in soot formation for differentfeedstock types and their blends with regular diesel. Furtherresearch needs to be carried out to understand the relationshipbetween the type of BD feedstock and performance andemission.

Acknowledgement

The authors express their gratitude to the United StatesDepartment of Transportation (USDOT) under Grant numberDTRT12-G-UTC21 and Mineta National Transit Research Con-sortium (MNTRC) for funding the BD study. The views exp-ressed in this paper are those of the authors alone and do notrepresent the views of the funding organizations.

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