Recent studies on soot modeling for diesel combustion

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

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Recent studies on soot modeling for diesel combustion Hamid Omidvarborna a, Ashok Kumar a, Dong-Shik Kim b,n a b

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

art ic l e i nf o

a b s t r a c t

Article history: Received 20 August 2014 Received in revised form 15 March 2015 Accepted 3 April 2015

This paper analyzes published works on the emission models of diesel and BD1 fuels. To the best our knowledge, this is the first comprehensive survey that reviews various modeling aspects of soot emitted from the combustion of diesel and BD fuels. The pros and cons of past and recent soot models, the chronological advancement of diesel combustion chemistry, and soot modeling approaches are highlighted in this review. Soot models are divided into three main groups of empirical, semi-empirical, and detailed soot model. Phenomenological model is also explored as a soot model which is one of the most extensively investigated soot models in recent years. Soot formation mechanism is discussed with an emphasis on their molecular structure. In a vast majority of the papers reviewed, acetylene was used as a soot precursor, and also as a reactant for soot mass growth and aromatics formation in diesel soot modeling studies. Thus, it is recommended that the formation and consumption of acetylene and aromatic compounds should be included in the diesel soot modeling. For BD, aromatic compounds are found at very low concentrations during the combustion, so the contribution of aromatic compounds to soot formation may be reduced or excluded in BD soot modeling. Unlike diesel, oxygen in BD fuels is found very important in soot oxidation, thus, formation and consumption of oxygen molecules, radicals and 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.

Keywords: Soot formation Particulate matter Combustion modeling Precursor formation Oxygenated fuel

Contents 1. 2. 3.

4.

n

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soot composition and structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soot formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Precursors for soot formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Nucleation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Mass growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Coagulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. Oxidation process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Empirical and semi-empirical soot models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Recent studies on soot modeling with emphasis on phenomenological studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Soot formation mechanism from oxygenated fuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Corresponding author. Tel.: þ 1 419 530 8084; fax: þ1 419 530 8086. E-mail address: [email protected] (D.-S. Kim). 1 Biodiesel. 2 Hydroxide bonds. 3 Methyl butanoate. 4 Methyl decanoate.

http://dx.doi.org/10.1016/j.rser.2015.04.019 1364-0321/Published by Elsevier Ltd.

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

1. Introduction

2. Soot composition and structure

The call for emission reduction has been mandated by many governments. For past few years, it has been one of the top priorities for combustion research centers to investigate combustion processes and emission reduction methods through optimizing the engines and fuels [1–3]. Soot modeling is regarded as an important part of understanding the process of soot formation, which in turn contributes to the development of effective emissions reduction techniques. Diesel is known as a source of emission species such as particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), heavy metals, and nitrogen oxide (NOx) [4,5]. These species are produced from combustion and found in emissions mainly in the form of aerosols, and are recognized as health hazards [6,7]. Among these diesel emission components, PM5 has been a serious concern for human health due to its direct and broad impact on the respiratory organs [4,5]. In earlier times, health professionals associated PM10 (diametero 10 mm) with chronic lung disease, lung cancer, influenza, asthma, and the cause of increased mortality rate [8]. However, recent scientific studies suggest that these correlations be more closely linked with fine particles (PM2.5) and ultra-fine particles (PM0.1) [9], because the fine and ultra-fine particles can easily penetrate deep into the lungs. To address these problems, a great deal of air quality research has been performed on toxicity and chemistry of PM over the last 40 years [10,11]. It is generally reported that the majority of PM is originated from soot, (highly carbonaceous material which weighs typically higher than 50% in PM mass), which is usually formed in fuel-rich or lowoxygen regions of a diesel engine [12–15]. A better understanding of the soot formation made it possible to formulate mathematical models that predict the concentration or mass of soot in the emissions, and validate the proposed mechanisms, and in turn good models are helpful in better understanding soot characteristics and formation mechanisms. Significant advances have been made on the mechanisms of soot formation in the last two decades [16–19]. However, it appears that there is still a gap between the existing soot models and actual soot formation processes. The gap becomes even greater when it comes to soot formation from combustion of oxygenated fuels (BD fuels) due to the varying compositions and diverse types of feedstock [20–22]. Soot modeling can be improved as long as the formation and oxidation mechanisms are clearly understood and accordingly more realistic assumptions are made. This review begins with a brief description of soot formation fundamentals and summarizes the progress of soot modeling for diesel combustion since the early 1970s, while giving more emphasis on the modeling work over the last 20 years. It is followed by different modeling approaches and comparison of those approaches along with the highlights of theoretical and empirical results. It also investigates the models' specifications and parameters, and then the accuracy of their predictions for the performance of the model with regard to the soot concentration. Not all of the available models in the literature are considered here, but the review focus is mainly on the practical soot models for vehicle combustion of diesel developed over the past two decades.

To better understand the mechanisms of soot formation found in the literature, it is worthwhile to briefly review the chemistry of the soot. Soot is a solid substance consisting of roughly eight parts of carbon and one part hydrogen (soot density is 1.84701 g/cm3 [23] and the reports by most other authors fall near this value). In urban areas soot is mostly formed as a result of fuel combustion in engines and its characteristics do not appear to be functions of fuel and other operating conditions [24]. Soot becomes a part of black carbon/smoke when present in sufficiently large particle size and quantity in exhaust gases. Soot nucleates from the vapor phase to a solid phase in fuel-rich regions at elevated temperatures [13,14]. HCs or other available molecules may condense on, or be absorbed by soot depending on the surrounding conditions [25]. A newly formed soot particle initially has the highest hydrogen content, and the C/H ratio is as low as one. However, as the soot matures, the carbon fraction increases. Trace amounts of zinc, phosphorus, calcium, iron, silicon, and chromium are also often detected 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 of necklace-like agglomerates [16]. Fig. 1a is a typical scanning electron microscope (SEM) image of diesel soot showing these agglomerates are composed of collections of smaller particle units in spherical or close to spherical shape [17]. X-ray diffraction (XRD), as illustrated in Fig. 1b [28], indicates that the carbon atoms of a primary soot particle are packed into hexagonal face-centered arrays, commonly referred to as platelets. Platelets are arranged in layers to form crystallites, and there are typically two to five platelets per crystallite [28]. When analyzed under high-resolution transmission electron microscopy (HRTEM), two distinct parts of a primary diesel soot particle can be identified: an outer shell and an inner core, as shown in Fig. 1c [29]. The platelet model mentioned above applies to the outer shell. However, the inner core contains fine particles with a spherical nucleus surrounded by carbon networks 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 due to the thermodynamic instability of its structure. Arrangements of crystallites which contain inner/outer shells and fine particles in collected soot (with different sizes observed under HRTEM analysis) are shown in Fig. 1c. In summary, the formation of soot, i.e. the conversion of HC fuel molecules into carbonaceous agglomerates, is an extremely complicated process. It is a kind of gaseous-solid phase transition where the solid phase exhibits no unique physical and chemical structures, and the transition occurs through various chemical reaction and physical interaction steps. A number of approaches to soot modeling exist, but there is a trade-off between the capability of predicting the details of soot formation and computational time. Another issue in soot formation modeling is the complexity of simultaneous chemical and physical phenomena, such as precursor formation from the gas phase chemistry, primary particle inception, nucleation, particle growth, coagulation and particle oxidation which are hard to describe in a series of mathematical formula. So, simplified soot models that can produce more realistic results in reduced computational time are highly desired 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 of matter in which a number of molecules undergo many chemical and physical reactions within a few milliseconds. It is still not

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clearly understood how soot particles and their precursors are formed despite the broad and extensive studies published in the literature [16,17,24,28]. Many details of soot formation chemistry remain unanswered and controversial, but there have been a few agreements 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.

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

Many references proposed various soot formation processes in which many of them have in common. These widely agreed mechanisms proceed in three steps and are depicted in Fig. 2. Large aromatic rings are formed mainly through addition of light HCs (acetylene) molecules in the molecular scale. Primary soot particles are supposed to be formed either by surface growth or coagulation of these larger aromatic compounds. Tree and Svensson [25] presented five steps of soot formation as depicted schematically in Fig. 3. In which, acetylene and PAH molecules are involved during precursor formation after fuel pyrolysis. Nucleation, surface growth, coalescence and agglomeration are considered as afterwards steps. According to the Tree and Svensson [25] soot formation mechanism, the HC fuel is degraded into small HC separate radicals. Later on HC radicals are added for growth of unsaturated HCs when they contain a sufficiently large number of carbon atoms in their structure. An increase in the acetylene concentration acetylene mainly helps the formation of larger aromatic rings. The growth is supposed to happen by coagulation of larger aromatic structures forming primary soot particles. These primary particles quickly coagulate, simultaneously picking up molecules from the gas phase for surface growth. Five common steps in soot formation are briefly discussed in next sub-sections. 3.1. Precursors for soot formation

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

The species that are considered to be the onset for soot formation and growth are referred to as precursors. Soot inception is a mechanism through which the precursors are resulted from fuel combustion to form soot particles. Inception is poorly understood because the nascent soot particles are extremely small (about 1 nm in diameter) thus making experimental investigations very difficult [18]. Among them acetylene has received great attention [30]. Acetylene has been identified by Glassman [24] and later confirmed by Richter and Howard [18] as a very important precursor for soot formation in diesel combustion, most likely because the first aromatic rings are formed from C2 and C3 additions [31]. In 1990s, Frenklach and Wang [32] proposed that the addition of acetylenes lead to the formation of first aromatic rings, and those aromatic rings are the soot precursors. Due to limited formation of some intermediate molecules via acetylene and complexity of experimental studies, PAHs molecules instead of

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 diesel combustion [33,34]. PAH formation and its growth appear to depend mainly on the type of fuel. Some of the reaction sequences which depict the formation of first aromatic rings are summarized elsewhere [26]. 3.2. Nucleation The next step, nucleation or inception of particles from heavy PAH molecules, bridges the transition from gaseous media in a combustion process to heavy molecules that eventually turn into nascent soot. The molecular mass of nascent soot is approximately 2000 atomic mass unit (amu) [16] with an effective diameter of about 1.5 nm (can be detected by HRTEM) [18], while it is commonly believed that nucleation starts at lower amu around 300–700 [35]. 3.3. Mass growth Soot surface growth is the overall mechanism through which soot particle masses grow via the addition of gas species such as acetylene and PAH molecules/radicals. There is no clear distinction between the end of the nucleation and the beginning of surface growth and in reality the two processes are concurrent. Frenklach [35,36] introduced the surface growth reaction mechanism back in 1980s. Soot particles undergo surface reactions with gaseous species via the hydrogen abstraction carbon addition (HACA) process [37,38]. For HACA growth, the soot surface property is an important factor in soot mass growth. C–H bonds on the surface of the soot interfere with H and OH radicals to form reactive sites, where gaseous molecules (particularly acetylene) can be added to the surface of the soot particle [37,39]. 3.4. Coagulation As depicted in Figs. 2 and 3, during nucleation, particle growth happens through the coagulation step, i.e., a combination of two or more particles to form a larger particle, sometimes called coalescence [17,25]. The results of experiments depict that particle coagulation process occurs almost immediately after the soot particle formation, or when soot particles are relatively small or young [17]. Sticking collisions between particles during the mass growth process significantly increase the particle size and decrease the number of particles without changing the total mass of soot present. Sometimes individual or primary particles stick together to form large groups of primary particles which maintain their shape. In this case the process is called agglomeration. So, the coagulation process forms a large particle by combining small particles, where during agglomeration the primary particles stick to each other, forming a group of chain-like aggregates. An example of agglomeration is easily found in the collection of exhaust soot from a diesel engine. In soot exhaust, soot consists of primary particles which are spherical in shape, and they are agglomerated 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 the mass of soot by converting the solid soot particles or part of them back into gases (e.g. CO and CO2). Oxidation is similar to the surface growth in a sense that the surface area of the particles affects the rate of oxidation. Oxidation takes place on the surfaces of soot particles and decreases the mass of soot and reduces the mass of carbon accumulated in the soot particles [17]. Unlike the surface growth of soot, which occurs in a specific step, oxidation

happens all the time during and after soot formation. Oxidizing elements are O, O2, and OH under fuel-rich conditions, but in fuellean media H2O, CO2, NO, N2O, and NO2 are also possible oxidants [19]. More oxidation models in which oxidants other than O2 are involved are presented elsewhere [19,40]. A comprehensive review for the fundamentals of soot formation mechanism is beyond the scope of this review. More in-depth reviews were provided by earlier studies [16,17,24,28].

4. Modeling Soot mechanism is difficult to be mathematically modeled because of the large number of primary components of diesel fuel, quite complex combustion mechanisms, and the heterogeneous interactions during soot formation [41]. Soot models are broadly categorized into three subgroups [42]. Empirical (equations that are adjusted to match experimental soot profiles), semi-empirical (combined mathematical equations and some empirical models which used for particle number density and soot volume and mass fraction), and detailed theoretical mechanisms (covers detailed chemical kinetics and physical models in all phases) are usually available in literatures for soot models. Empirical models use correlations of experimental data to predict trends in soot production [43–45]. Empirical models are easy to implement and provide excellent correlations for a given set of operating conditions. However, empirical models cannot be used to investigate the underlying mechanisms of soot production. So, these models are not flexible enough to handle changes in operating conditions. They are only useful for testing previously established designed experiments under specific conditions. Second, semi-empirical models solve rate equations that are calibrated using experimental data [43,46,47]. Semi-empirical models reduce computational costs primarily by simplifying the chemistry in soot formation and oxidation. Semi-empirical models reduce the size of chemical mechanisms and use simpler molecules, such as acetylene as precursors. Detailed theoretical models use extensive chemical mechanisms containing hundreds of chemical reactions in order to predict concentrations of soot. Detailed theoretical soot models contain all the components present in the soot formation with a high level of detailed chemical and physical processes. Such comprehensive models (detailed models) usually take high financial burden for programing and operating, and much computational time to produce a converged solution. On the other hand, empirical and semi-empirical models ignore some of the details in order to make complex model simple and to reduce the computational cost and time. Thanks to recent technological progress in computation, it becomes more feasible to use detailed theoretical models and obtain more realistic results. However, further advancement of comprehensive theoretical models must be preceded by the more detailed and accurate formation mechanisms. On the other hand, models that are based on a phenomenological description have found wide use recently. Phenomenological soot models, which may be categorized as semi-empirical models, correlate empirically observed phenomena in a way that is consistent with the fundamental theory, but is not directly derived from the theory. Phenomenological models use sub-models developed to describe the different processes (or phenomena) observed during the combustion process. These sub-models can be empirically developed from observation or by using basic physical and chemical relations. Advantages of phenomenological models are that they are quite reliable and yet not so complicated. So, they are useful, especially when the accuracy of the model parameters is

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low. For example, as presented by Argachoy and Pimenta [48], the phenomenological models can predict the soot formation even when several operating conditions are changed in a system and the accuracy cannot be guaranteed. Examples of sub-models of phonological empirical models could be listed as spray model, liftoff 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 on empirical and semi-empirical approaches. Several models proposed in these categories consider two competing reactions, soot formation and soot oxidation, as a two-step approach [40,47,49]. Both formation and oxidation rates are highly temperature dependent, and they are represented by Arrhenius type expressions. Soot formation rates are proportional to fuel vapor pressure while formation expressions contain no dependence on the type, composition or structure of fuel. Also, the two-step models contain no information on particle size or agglomeration of soot, both of which affect the surface area of soot available for a given mass of soot produced as explained in Section 3. The oxidation expression includes only O2, leaving out other important oxidation mechanisms such as OH reactions. A study on soot formation by Tesner et al. in 1971 [46] was one of the first soot models in these categories that include a branched-chain process and soot particle formation. Tesner et al.'s model implemented an idea that soot is formed as a result of adsorption of radical nuclei on the precursor surface. In the same year, an empirical model was proposed by Khan et al. [44,50]. This model predicts soot emissions from diesel engines based on the assumption that the rate of soot production is entirely a function of soot nucleation rate. It means that the rates of particle growth and oxidation were neglected in this model. Also, the model assumes that the diameter of a soot particle is not a function of engine operating conditions at different speeds or loads, which is regarded as unrealistic. The model includes some modeling parameters determined by comparing the output of the model with experimental data [42,44]. A few years later, Hiroyasu et al. [49] proposed one of the most widely used models that are essentially based on empirical formulas for predicting the formation and oxidation of soot particles. They found that their two-step soot model was primarily affected by pressure, temperature, and equivalence ratio, which is the actual fuel to oxidant ratio normalized by the stoichiometric fuel to oxidant ratio. The model includes a soot formation and oxidation rate, which incorporates the available fuel mass and oxygen partial pressure. Eq. (1) calculates the first order rate of net soot formation (dms/dt) using a combination of soot formation rate and soot oxidation rate for Hiroyasu's model. Then, the soot formation rate equation (Eq. (2)) follows an Arrhenius-type relation with the mass of vaporized fuel (mfg). Oxidation rate of soot (dmsc/dt) in Eq. (3) also follow an Arrhenius-type equation as a function of mass of soot in the system (ms) and oxygen pressure ðP o2 Þ. The oxidation rate of soot is almost a second order (1.8) of pressure whereas the formation rate of soot is a half order of pressure

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and soot oxidation, respectively. Af and Ac were determined by matching the calculated soot and experimental soot in the exhaust. The Hiroyasu's model has been very helpful in providing knowledge on the bulk distribution and transport of the soot in the high-temperature combustion environments of conventional diesel engines [51]. Moreover, owing to ease of implementation into computational fluid dynamic (CFD) codes, this model and its modifications have acquired wide popularity in the community engaged in multidimensional diesel combustion simulations [52]. The two-step approach of Hiroyasu's model [49] is regarded oversimplified for the diesel soot formation processes because they proposed a two-step empirical soot model for predicting the formation and oxidation of soot particles and the model underpredicted the peak in-cylinder soot concentration [51]. Soot formation formula of Hiroyasu's model contains no dependence on the type, composition or structure of fuel. The oxidation expression includes only oxygen molecules in the model [53]. Hiroyasu's model is regarded very practical and simple, but it needs more parameters to be upgraded for further studies [54– 56]. Another basic semi-empirical model developed for diesel engines 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 intermediate species. It means that the soot surface growth rate was determined by experimental data on a final soot volume. The Hiroyasu-Nagle and Strickland (HNS) soot model has been another very popular two-step semi-empirical model for soot formation in diesel engines [47,61]. The rate of soot formation and oxidation were expressed again in an Arrhenius-type equation as follows: Rate of formation ¼ Af P 0:5 e  Ef =RT

ð4Þ

Rate of oxidation ¼ X O2 Ao P 1:8 e  Eo =RT

ð5Þ

dms dmsf dmsc ¼  dt dt dt

ð1Þ

where Af and Ao are the pre-exponential factors; Ef and Eo are the activation energies; X O2 is oxygen molar fraction; R is the ideal gas constant; and T is the gas temperature. Hiroyasu's approach is adopted to describe soot formation while its oxidation is estimated by the Nagle-Strickland and Constable model [54,55]. Moss [62] also presented a semi-empirical two-step model which is slightly different from the previous models. In this model, not only nucleation and oxidation rates, but also coagulation and growth rates, were considered and implemented in the process of soot formation. The superiority of Moss's model is not surprising since it simulates the processes of nucleation, surface growth, and coagulation, 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 growth of soot particles [63]. Detailed gas phase chemistry and simplified steps of nucleation, surface growth and particle agglomeration were incorporated in the model. The soot nucleation and surface growth reactions are linked to the gas phase chemistry. Lindstedt paid much attention to the problems in modeling the soot mass growth. Lindstedt considered four models for the soot growth reactions [42]. More details on the recent soot models are provided later in Section 4.2 that addresses detailed mechanisms and phenomenological soot models as well.

dmsf ¼ Af mf g P 0:5 eð  Esf =RT Þ dt

ð2Þ

4.2. Recent studies on soot modeling with emphasis on phenomenological studies

dmsc Po ¼ Ac ms 2 P 1:8 eð  Esc =RTÞ dt P

ð3Þ

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

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

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and Khan et al. [50,65] presented ones of the earliest models for the prediction of soot production from a diesel engine, variety of soot models with different levels of complexity have been proposed and applied to soot formulation. As mentioned earlier soot formation and oxidation are very complex, and it is not possible to exactly model a complex process only with simplified models. Unlike the empirical and semiempirical models which extremely simplify the soot formation process, the detailed model describes the formation, growth, and oxidation of soot with a detailed chemical reaction mechanism. The extremely high demand of computing time of detailed soot models makes them unrealistic for simulation of diesel engine combustion. Hence, most of the investigations conducted in a real configuration such as multidimensional diesel engines utilize coarse modeling schemes to take advantage of easy implementation and low computational cost. This section reviews the published papers that focused on the detailed models and those with the emphasis on phenomenological methods. With recent advances in computer technology and developments in mathematical sub-models, it is now possible to obtain useful predictions and visualizations of complex systems. Therefore, numerical simulation of such complex systems is considered in many areas. In soot modeling, numerical simulations can also be divided into two main classes: phenomenological modeling and multidimensional CFD modeling. In phenomenological modeling, the spatial variations are often simplified by zero-dimensional or one-dimensional models, while multidimensional modeling is designed to take into account all the spatial variations of reactive fluid flow in diesel engines simultaneously [26]. In order to improve the accuracy and predictability, phenomenological multi-step soot models have been implemented in many empirical, semi-empirical and detailed models. In other words, phenomenological models describe the complex process of soot formation and oxidation in terms of several global steps that are particularly advantageous for practical combustion simulations. In 1994, Fusco et al. [66] proposed a phenomenological soot model to overcome some limitations of the previous soot models for combustion conditions of a diesel engine. The model accounts for the number of carbon atoms of the major constituent molecules in the fuel and incorporates the physical process of inception, surface growth, coagulation and oxidation into the eight-step phenomenological soot model. They also compared their model with the existing two-step empirical models and criticized the non-applicability of the two-step empirical models for a wide range of operation conditions in diesel engines. The model consists of four differential equations balancing between the rates of particle number change, soot precursor radicals, acetylene and soot volume fraction. Just like the previous formulations, Arrhenius-type rate expression has been used for most of the Inert products (3) (1) Pyrolysis

Oxidation

Soot precursor radicals

Inert products Inception

(5)

Fuel

(2)

(4)

Oxidation

Oxidation

Soot particles

(6) Growth species (acetylene)

(7)

Surface growth

(8)

Coagulation

Soot particles

Inert products Fig. 4. Schematic diagram of eight-step phenomenological soot model presented by Fusco et al. [66].

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

processes except for coagulation and oxidation steps. The surface growth species, which was assumed to be acetylene, enhances the mass of the soot particles. Schematic diagram of the phenomenological model is presented in Figs. 4 and 5. Fig. 4 shows that the portion of the fuel is converted to soot precursor radicals (R1) and growth species (C 2 H 2 by R2). Radicals and growth species are considered to be separated species, although they could be the same species at least at the beginning of the soot formation process. A portion of the precursor radicals are oxidized (R3) and the rest are converted to soot particles (R5). The growth species increase the mass of soot particles by R6. Oxidation is assumed that it does not affect the particle number density. Growth species (C 2 H 2 ) may disappear, and the mass of soot particles may decrease via oxidation (R4 and R7), respectively. The number of soot particles can decrease due to coagulation with other soot particles (R8). Their modeling result demonstrated that more soot is produced when more acetylene is available. This result means that the final amount of soot depends on the balance between soot formation and oxidation in the both solid and gas phases. In 1998, a modified version of the phenomenological model of soot formation by Kazakov and Foster [43] has been implemented into the model developed by Fusco et al. [66]. The model includes major generic processes involved in soot formation during combustion; formation of soot precursors, soot particle nucleation, coagulation, surface growth, and oxidation. After Kazakov and Foster, Fusco's original model [66] extended by Liu et al. [67] to produce a nine-step model as presented below: 1. 2. 3. 4. 5. 6. 7. 8. 9.

Acetylene formation from fuel pyrolysis. Soot precursor formation from acetylene. Particle inception from soot precursors. Soot particle coagulation. Surface growth from acetylene. Oxidation by O2. Oxidation by OH. Acetylene oxidation by O2. Precursor oxidation by O2.

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

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develop a nine-step model [67]. The nine-step model had a fundamental weakness which is still unable to express the role of fuel composition and structure whereas each of the acetylene formation rates is reported to be dependent on fuel structure [25]. Conventional diesel fuel contains around 30–35% PAHs, and numerous studies in recent years have demonstrated that aromatics such as PAHs play a key role in the soot formation process in diesel engines [68,69]. Idicheria and Pickett [69] focused on the role of PAH in soot formation in diesel combustion. They came to the conclusion that PAH chemistry might play an important role both for accurate prediction of soot mass and distribution. Therefore, the chemical kinetic mechanisms of the mixture of n-heptane and aromatics (to present as a diesel surrogate) included in recent modeling studies are considered more accurate to simulate soot formation than previous models. Since it is not practically possible to have kinetic reaction mechanisms for all of the hundreds or even thousands of species present in conventional diesel fuel, n-heptane is used in many modeling studies of diesel combustion as a diesel surrogate [30,70,71]. N-heptane has a cetane rating of 56 that is typical of ordinary diesel fuels, and that is why the combustion process in diesel engines has often been simulated using n-heptane as a surrogate diesel fuel [71]. In Tao et al.'s [70] model, diesel fuel is assumed to be singlecomponent, and its oxidation chemistry is represented only by the n-heptane kinetics. The chemical mechanism simplified to a size of 65 species and 273 elementary reactions. Here formation reactions of PAHs (up to few aromatic rings) from acetylene were considered as initiator for soot formation in soot modeling. In 2002, Frenklach assumed the formation and growth of PAHs as the first step in soot formation [37]. In this model, the particles grow via surface reactions similar to the growth reactions for PAHs, i.e., mainly by the HACA mechanism. When particles collide with each other, either they form new spherical particles or agglomerates. In that paper, the discussion shifted from phenomenological possibilities to specifics of reaction pathways. In a parallel study with Liu et al. [67], nine-step phenomenological soot model was updated for predicting soot formation and oxidation processes in diesel engines by Tao et al. [72]. The brand new model presented by Tao and coworkers consist of two parts, detailed chemical reaction mechanism and a phenomenological semi-empirical soot model. Tao's model retains the main features of his original model [43,66], but contains three major modifications: (1) fuel pyrolysis leads solely to acetylene formation; (2) the soot precursor is formed merely via acetylene (i.e., not directly from fuel); (3) an OH-related soot oxidation step is added. In the earlier study of Liu et al. [67], the OH concentrations were calculated using the concept of chemical equilibrium, the assumption of which was unfortunately unrealistic when applied to transient diesel combustion processes. The updated nine-step soot model [72] was successfully applied to analyze the soot distribution structure in a conventional diesel for a benchmark heavy-duty diesel engine (Cummins) based on which a comparison to the two-step soot model was attempted. In 2010, Vishwanathana and Reitz [73] presented a practical model framework only based on four fundamental steps: soot inception through a four-ring PAH species, surface growth of acetylene, coagulation of acetylene to form soot, and soot oxidation via oxygen and OH. They concluded that the soot model is fairly sensitive to the PAH chemical mechanism [74]. Simultaneously, Cheng et al. [75] presented an improved detailed soot model for the numerical investigation of soot formation, mass concentration, and size distribution in diesel engines. The effects of soot precursors, including isomers of

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acetylene and PAHs, and the physical processes of PAH deposition on the particle surface, soot formation, and particle surface growth were considered into the model. They found that large amounts of small-size soot particles (in the range of 5–40 nm) were produced at the initial stage of combustion by the pyrolysis reactions and polymerization of the HC fuel. In the intermediate stage of combustion, soot particles continued to grow by particle coagulation, surface growth, and the deposition of PAHs. In the final stage of combustion, the particle size distribution stabilized in the range of 5 to 20 nm due to the influence of further oxidation reactions. Jia et al. [76] quantitatively validated and improved the phenomenological soot model over wide operating conditions of homogeneous charge compression ignition (HCCI) combustion. The phenomenological model developed in this research as summarized in Table 1 is based on the work of Tao et al. [77]. By comparing experimental results, necessary improvements have been made to the model for describing the soot formation process under various conditions. The complex processes of soot formation and oxidation are divided into several steps including acetylene (C 2 H 2 ) formation from pyrolytic decomposition of fuel, precursor formation via C 2 H 2 conversion, particle inception from precursor, particle surface growth by C 2 H 2 , particle coagulation, particle surface oxidation via oxygen (O 2 ) and OH. Later, a six-step phenomenological soot model with particle dynamics was developed by Pang et al. [78]. The sub-model for soot formation was constructed based on Jia's soot model [104] by introducing necessary improvements and optimizations. The schematic representation in Fig. 6 shows the structure of the six-step phenomenological soot model developed in their study. Soot formation and oxidation process are divided into several steps including soot precursor formation via C 2 H 2 , A3 (aromatic structure with 3 rings) and A4 conversion, particle inception from soot precursor, particle surface growth by C 2 H 2 and A1, particle coagulation, particle surface oxidation via O 2 and OH, and precursor oxidation. The new model retains the main features of the original one [76] but two major modifications are 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 particle are represented by C(PR) and C(S), respectively. The rates of reactions, 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 to be in the form of Arrhenius equation. In this approach, this phenomenological soot model has gained significant improvements in performance by incorporating the PAH chemistry into the model. A modified skeletal PAH mechanism for the phenomenological soot formation was integrated into a primary reference fuel (PRF) oxidation mechanism where A3 and A4 were the soot precursor species. The new skeletal PAH mechanism is capable of describing the formation process of PAHs beyond A1 and up to A4. As pointed out by Vishwanathan and Reitz [73] and Ra and Reitz [79], the PRF mechanism developed earlier by Ra and Reitz [80] under-predicts the concentration of C2H2, which is considered as an important species in soot inception and growth processes. If a PAHs mechanism is applied, C2H2 is also an important species that affects the PAH growth process through the well-known HACA growth mechanism [81]. Table 1 summarizes the recent modeling approaches and their results that are presented in this paper.

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Table 1 Concise description of works done on soot modeling mainly for diesel engines. Author name (year) Belardini et al. [41] Frenklach et al. [82,83]

Model specification

N-heptane is selected as a diesel surrogate. Assuming acetylene as the crucial pyrolitic species. The model combines recent developments in gas-phase reactions, aromatic chemistry, soot particle coagulation, soot particle aggregation, and develops a new sub-model for soot surface growth. Daly and Nag A new gas phase kinetic model using Westbrook's gas phase n-heptane [84] model and Frenklach's soot model [82,83]. 614 Species and 2883 reactions are involved in the complex reaction mechanism. Tao et al. [70] Diesel fuel is assumed to be single-component, and its oxidation chemistry is represented by the n-heptane kinetics. The chemical mechanism reduced to a size of 65 species and 273 elementary 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. The soot emission process is simulated by a phenomenological soot model that uses a competing formation and oxidation rate formulation. Acetylene is selected as a soot precursor. Boulanger et al. A phenomenological three-equation soot model in diesel engine [85] combustion.

Results/notes

The model over-predicts both acetylene and soot volume fraction. Better results can be obtained by model's constant optimization. The surface growth and oxidation of soot particles are described consistently with the kinetics of gaseous PAHs. It represents the state of the art in detailed soot modeling for diesel combustion.

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

Both experiments and models reveal that soot emissions peak when the start of injection (SOI) occurs.

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.

A reduced n-heptane chemistry mechanism has been extended to include Soot formation and growth regions are not adequately represented by PAH species up to four fused aromatic rings (pyrene). using acetylene alone as the soot inception species. Various soot inception species have been tested. A simpler model that only considers up to phenanthrene (A3) as the soot inception species has good possibilities for better soot location predictions. Tao et al. [72] Nine-step phenomenological soot model. Nine-step model is not only computationally efficient but also Model includes a detailed chemical reaction mechanism and a fundamentally sound. phenomenological semi-empirical soot model. After calibration, the model is suitable to be integrated with genetic algorithms for system optimization over a controllable range of operations. Mosbach et al. A detailed model for the formation of soot in internal combustion engines A detailed chemical kinetic mechanism describing the combustion of PRFs is extended to include small PAHs such as pyrene, which function as [86] describing not only bulk quantities such as soot mass, number density, soot precursor species for particle inception in the soot model. volume fraction, and surface area but also the morphology and chemical composition of soot aggregates. Even with a detailed chemical mechanism, soot formation and oxidation Jia et al. [76]) An improved phenomenological soot model coupled with a reduced n-heptane chemical to describe soot formation and oxidation processes in still remain as challenges. HCCI combustion. The phenomenological soot model coupled with reduced fuel chemical mechanism showed satisfactory agreement with the experiments. Vishwanathana Reduced n-heptane and PAH chemistry mechanisms are formulated from The model is based on four fundamental steps: soot inception through a the literature. four-ring PAH species, surface growth through acetylene, soot and Reitz Acetylene was selected as a soot precursor. coagulation, and oxygen- and OH-induced soot oxidation. [73,74] Sukumaran A multistep soot model coupled with reaction mechanisms for fuel Soot emissions from the engine are highly sensitive to local temperature et al. [87] oxidation and PAH formation. and chemical compositions. N-heptane mechanism is combined with a detailed PAH mechanism, by choosing pyrene as precursor. The overall reaction mechanism consists of 68 species and 145 reactions and is used with a multistep soot model. Pang et al. [78] 12 Species and 26 reactions for the formation of PAH are integrated into a The results prove a very good agreement with experimental data and the skeletal mechanism for the oxidation of PRF (n-heptane and iso-octane). necessity of including PAHs chemistry for soot modeling. Six-step phenomenological soot model with PAHs (A3 and A4) as a precursor. Particle surface growth by A1 is added in the new soot model. Naik et al. [88] N-hexadecane, heptamethylnonane, 1-methylnaphthalene, and decalin A new pseudo-gas soot model coupled with the fuel chemistry to are used to represent standard European diesel. simulate an in-cylinder soot nucleation, growth, and oxidation processes. A validated detailed surrogate mechanism containing 392 species and 2579 reactions was employed to model the chemistry of fuel combustion and emissions. Analyses are conducted under low-temperature combustion (LTC) condition. Cheng et al. [75] An improved soot model coupled with a detailed mechanism of reduced The particle emissions increase with increasing engine load. diesel surrogate fuel (n-heptane / toluene). Chemical kinetic mechanism contains 70 species and 313 reactions. Particle concentration and average particle size significantly increase at Isomers of acetylene and PAHs are selected as precursors. the starting stage of the combustion process and quickly stabilize. Vishwanathan and Reitz [34]

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

4.3. Soot formation mechanism from oxygenated fuels Investigations of alternative fuels for internal combustion engines have recently become important due to the growing concerns about the future availability of oil reserves and environmental sustainability. Among them, biofuel is receiving increasing public and scientific attention especially from the transportation sector driven by its carbon neutrality and comparability with existing engines [13,14,20,27,89,90]. Several investigators have concluded that the structure of oxygenated fuel has an effect on the amount of soot reduction possibly achieved with a given amount of the oxygen atoms included in the fuel structure [13,91,92,93]. The oxygenated fuels such as BD usually consist of fatty acid methyl esters (FAMEs)/fatty acid ethyl esters (FAEEs), and they should be considered for their reactions in soot modeling. FAMEs and FAEEs are produced through the transesterification process of vegetal oils or animal fat with methanol or ethanol as a catalyst. FAME has lower energy content than diesel due to its high oxygen/low carbon contents, and as a result, its combustion and fuel consumption can be affected accordingly. The main fatty acids in rapeseed and soybean oils are oleic (C18:1 monounsaturated) and linoleic (C18:2 polyunsaturated) acids [94]. Compared to regular diesel, the oxygenated structure of BD enhances oxidation process in soot formation and dramatically reduces the mass of soot. On the other hand, kinetic studies of BD surrogates showed that early CO2 production from the methyl ester (ME) group in methyl decanoate (MD) has important impacts on ignition and soot production, because if the oxygen in the fuel immediately produces CO2, it becomes less effective in reducing soot production [95]. Therefore, it is necessary to develop well validated models for the combustion and the oxidation of the oxygenated components of BD to account for the effect of extra oxygen on soot formation. Among the emission studies conducted on oxygenated fuels, Miyamoto et al. [91] concluded that soot reduction was related to the oxygen content of the fuel and not to the type of fuel. Also, the study of Song et al. [93] on soot emissions from oxygenated fuels showed that the operating conditions of a diesel engine would change the morphology of soot and it would produce smaller particles in idle mode while using oxygenated fuel. Not many papers have been published on soot formation modeling for oxygenated fuels such as BD compared to regular diesel modeling [96–98]. Although the effect of oxygen content on soot formation and oxidation process are considered highly

107 108 109 105 105 91 110 110

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

important in soot generation from BD combustion, it is also expected that the widely differing physical properties of BD from regular diesel will influence the combustion mechanism and the related emissions formation. But, a basic understanding from the kinetic modeling of oxygenated molecules presented in these studies may provide clues for soot reduction processes in BD combustion. BD surrogates are mixtures of one or more simple fuel components that are designated to emulate physical and chemical properties of BD. While surrogate mixtures can demonstrate more than one characteristic of the fuel to be simulated, more often than not many other components are required to emulate the wide variety of properties that are of interest to researchers. As mentioned earlier n-heptane is normally used for the diesel surrogate while usually M9D (methyl 9 decenoate), n-butanol, and MB (even if the small size of these molecules prevents them from the combustion chemistry of the large molecules present in BD) are extensively used for BD [96,99]. As mentioned above, unsaturated esters are the most abundant esters in BD, but very few studies have been dedicated to combustion modeling for unsaturated esters [100]. As stated above, the oxygen content is an important factor to be investigated in BD study because the oxygen content in fuel not only provides more oxygen to burn carbon, but also displaces and reduces the amount of carbon that needs to be burned. In addition,

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considering the fact that PAH is a main cause for soot formation, BD has an advantage of low PAH formation as BD was observed to emit lower PAH emissions than normal diesel [101]. It was reported that potential for soot precursor formation disappears almost completely at an oxygen-fuel ratio of 25 wt% [102], and drops to an insignificant level with extra 30–40 wt% oxygen in a fuel [103]. It means that the BD's molecular structure and its oxygen content should be the main factors for soot precursor formation. A summary listing of references and fuels including BD is presented by Tree and Svensson [25] (Fig. 7), which is sufficient to draw several conclusions on the effects of oxygen contents on soot formation. It should be recognized that the results are gathered from the in-cylinder soot data while others are obtained from the exhaust emission data. Some of the findings are as follows: 1) Soot emissions are reduced by increasing fuel oxygen contents, based on Smith's study [104]. The reason would be because of oxidization of acetylene to relatively inert products in the presence of enough O2 and OH. 2) More than one study has demonstrated a complete or near complete elimination of soot when fuel oxygen content reaches 30% or more. 3) Scattered data points indicate that mass fraction of oxygenated fuel can have various amounts of percent PM reduction. 4) Miyamoto et al. [91,105] and Nabi et al. [106] have reported a nearly linear decrease in soot concentration as fuel oxygen contents increase. The linear relationship is very interesting because the required oxygen content to burn the fuel should be related to molar quantities for oxygen and carbon, not the mass of them. According to Curran et al. [103], n-heptane was used as a representative surrogate of diesel fuel, and methanol, ethanol, dimethyl ether, dimethoxymethane and MB were used as oxygenated fuel additives to simulate the oxygenated contents. It was found that when the overall oxygen content in the fuel reached approximately 30–40% by mass, production of soot precursors fell apparently to zero. Later on Mueller and coworkers [111] explored characteristics of soot and soot-precursor formation from oxygenated fuels (di-butyl maleate and tri-propylene glycol methyl ether). They defined four goals for their study: 1. To introduce the “oxygen ratio” for accurate quantification of reactant-mixture stoichiometry for both oxygenated and nonoxygenated fuels. 2. To provide experimental results demonstrating that some oxygenates are more effective at reducing diesel soot than others. 3. To present results of numerical simulations showing that detailed chemical-kinetic models without complex fluid mechanics can capture some of the observed trends in the sooting tendencies of different oxygenated fuels. 4. To provide further insight into the underlying mechanisms by which oxygenate structure and in-cylinder processes can affect soot formation in diesel engines. In 2013, a simplified chemical reaction mechanism was developed for modeling the combustion process and soot emissions for both non-oxygenated and oxygenated HC fuels by Wang et al. [96]. The final mechanism consists of 76 species and 349 reactions [96]. They reported that soot emission can be greatly reduced by addition of n-butanol. By blending n-butanol into a non-oxygenated HC fuel, air entrainment is enhanced by reducing the overall fuel to air ratio by introducing extra available oxygen atoms through the n-butanol molecule. The predicted

soot emissions under various conditions agree quite well with the experimental results. Inspection of the published results leads to the conclusions as follows [25]:

 There is a reduction in soot emissions with increased fuel oxygen in all cases.

 More than one investigator has demonstrated a complete or  

 

near complete elimination of soot when the fuel oxygen content reaches 27–35%. The scattering of experimental data verifies that a given fuel oxygen mass fraction can have various levels of soot reduction. Some authors [91,105,106,107,110] had observed a nearly linear decrease in soot concentration as oxygen contents increase while others [92,108] found decreasing slopes with decreasing benefits for soot reduction when oxygen contents increased. PAHs are known as soot precursors in diesel fuels, whereas they are detected at very low-concentrations in BD combustion [19,26,106,107]. BD has an advantage of low soot formation as BD was observed to emit lower PAH emissions than diesel fuel. Oxygen content plays an important role in lowering emissions of PAHs and it makes BD reactions more complex than diesel fuels. Therefore, including the detailed precursor formation and oxidation reaction in the BD combustion helps better model the soot formation mechanism.

The high oxygen content of BD makes extra oxygen available to facilitate the combustion of fuel, especially in the areas of very rich in fuel. It can have a favorable effect on less occurrence of pyrolysis and more enhancement of soot oxidation in comparison with regular diesel combustion. In addition, a wide diversity of feedstock selections for BD necessitates the inclusion of the effect of basic components of BD on combustion in the soot formation mechanisms and modeling. Because the major fuel components of BD from soybean oil, for example, are much different from the BD from tallow oil or waste cooking oil, and these feedstock-specific effect should be included in the soot modeling of BD.

5. Conclusion Fundamental concepts and models about soot mechanism in diesel and BD emission from combustion are examined in this review paper. Fuel combustion process is very complex, and their detailed mechanisms on soot are not quite well understood. From the literature review, it can be observed that the emission of soot from the BD-fueled engine is less than ULSD. However, more experimental and theoretical studies are needed to describe the complicated process of soot mechanisms in BD combustion. Soot formation and oxidation steps were incorporated in early studies on soot mechanism. Later on, due to emerging new computational systems along with modern experimental and analytical tools, common steps in soot mechanism were identified. Following these steps results in more accurate results compared to experimental data as described earlier. Among the proposed models for different combustion systems, empirical and semi-empirical soot models are found relatively simple and practical for specific systems where experimental results have been implemented into the models. In recent years, phenomenological models have been found to be more effective tool for simple and easy prediction of soot mechanism, but case by case adjustment of the implemented parameters may be needed. Detailed models were introduced as the most accurate and comprehensive models. These models require a great deal of computational time and cost.

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It is concluded that modeling of acetylene and its isomers as a starting component for formation of soot precursors seems to be a reasonable approach to the diesel soot modeling. The size of the model is important in determining the amount of computational time in detailed models, because the rate of soot precursor formation for each fuel will be dependent on fuel structure. Regarding soot formation in BD combustion, molecules such as MB, MD, n-heptane, and MS were identified as common fuel surrogates in modeling. The degrees of oxygenation and saturation (i.e., the number of double bonds) of BD fuels appear to be the important factors to be included in BD soot modeling. This is because of the number and position of double bonds which may effect the reaction pathways and mechanisms. For the BD soot modeling, due to lower emission of PAHs, the oxidation of precursors such as PAHs may be excluded. On the other hand the effect of early CO 2 production in BD combustion on soot formation should be considered. Modeling of soot formation has to address all these aspects of regular and BD combustions. In order to develop more robust and reliable models for soot mechanism, it is recommended that more reasonable assumptions be made based on a better understanding of chemical and physical interactions in soot mechanism. A fundamental challenge in soot modeling for BD is the inability to predict differences in soot formation for different feedstock types and their blends with regular diesel. Further research needs to be carried out to understand the relationship between the type of BD feedstock and performance and emission.

Acknowledgement The authors express their gratitude to the United States Department of Transportation (USDOT) under Grant number DTRT12-G-UTC21 and Mineta National Transit Research Consortium (MNTRC) for funding the BD study. The views expressed in this paper are those of the authors alone and do not represent the views of the funding organizations. References [1] Xue J, Grift TE, Hansen AC. Effect of biodiesel on engine performances and emissions. Renew Sustain Energy Rev 2011;15:1098–116. [2] Enweremadu CC, Rutto HL. Combustion, emission and engine performance characteristics of used cooking oil biodiesel—a review. Renew Sustain Energy Rev 2010;14:2863–73. [3] Shahabuddin M, Liaquat AM, Masjuki HH, Kalam MA, Mofijur M. Ignition delay, combustion and emission characteristics of diesel engine fueled with biodiesel. Renew Sustain Energy Rev 2013;21:623–32. [4] Krzyzanowski M, Kuna-Dibbert B, Schneider J. Health effects of transportrelated air pollution. Copenhagen, Denmark: World Health Organization, WHO Regional Office for Europe, Scherfigsvej 8; 2005. [5] Kunzli N, Kaiser R, Medina S, Studnicka M, Chanel O, Filliger P, et al. Publichealth impact of outdoor and traffic-related air pollution: a European assessment. Lancet 2000;356:795–801. [6] Kaden DA, Hites RA, Thilly WG. Mutagenicity of soot and associated polycyclic aromatic hydrocarbons to Salmonella typhimurium. Cancer Res 1979;39:4152–9. [7] Durant JL, Busby WF, Lafleur AL, Penman BW, Crespi CL. Human cell mutagenicity of oxygenated, nitrated and un-substituted polycyclic aromatic hydrocarbons associated with urban aerosols. Mutat Res 1996;371:123–57. [8] Stöber W, Abel UR. Lung cancer due to diesel soot particles in ambient air? Int Arch Occup Environ Health 1996;68:S3–61. [9] Schwartz J. Air pollution and daily mortality: a review and meta analysis. Environ Res 1994;64:36–52. [10] Mauderly JL. Toxicological and epidemiological evidence for health risks from inhaled engine emissions. Environ Health Perspect 1994;102:165–71. [11] Salvi S, Blomberg A, Rudell B, Kelly F, Sandstrom T, Holgate ST. Acute inflammatory responses in the airways and peripheral blood after shortterm exposure to diesel exhaust in healthy human volunteers. Am J Respir Crit Care Med 1999;159:702–9.

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