Multivariate analysis of performance and emission parameters in a diesel engine using biodiesel and oxygenated additive

Energy Conversion and Management 201 (2019) 112183

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Multivariate analysis of performance and emission parameters in a diesel engine using biodiesel and oxygenated additive

T

Mohammad Jafaria,b, Puneet Vermaa,b, Timothy A. Bodiscoc,d, Ali Zarec,d, Nicholas C. Surawskie, Pietro Borghesanif, Svetlana Stevanovicc,d, Yi Guob, Joel Alroeb, Chiemeriwo Osuagwub, ⁎ Andelija Milicb, Branka Miljevicb, Zoran D. Ristovskia,b, , Richard J. Browna a

Biofuel Engine Research Facility (BERF), Queensland University of Technology, Brisbane, Queensland 4000, Australia International Laboratory of Air Quality and Health (ILAQH), Queensland University of Technology, Brisbane, Queensland 4000, Australia c Flow, Aerosols and Thermal Energy Group (FATE), Deakin University, 75 Pigdons Road, Waurn Ponds, Victroia, 3216, Australia d School of Engineering, Deakin University, 75 Pigdons Road, Waurn Ponds, Victroia, 3216, Australia e Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, New South Wales 2007, Australia f School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales 2052, Australia b

A R T I C LE I N FO

A B S T R A C T

Keywords: Biodiesel Principal component analysis NOx Particulate matter Particle reactivity Acoustic emission

Rising concerns over environmental and health issues of internal combustion engines, along with growing energy demands, have motivated investigation into alternative fuels derived from biomasses, such as biodiesel. Investigating engine and exhaust emission behaviour of such alternative fuels is vital in order to assess suitability for further utilisation. Since many parameters are relevant, an effective multivariate analysis tool is required to identify the underlying factors that affect the engine performance and exhaust emissions. This study utilises principal component analysis (PCA) to present a comprehensive correlation of various engine performance and emission parameters in a compression ignition engine using diesel, biodiesel and triacetin. The results show that structure-borne acoustic emission is strongly correlated with engine parameters. Brake specific NOx, primary particle diameter and fringe length increases by increasing the rate of pressure rise. Longer ignition delay and higher engine speeds can increase the nucleation particle emissions. Higher air-fuel equivalence ratio can increase the oxidative potential of the soot by increasing fringe distance and tortuosity. The availability of oxygen in the cylinder, from the intake air or fuel, can increase soot aggregate compactness. Fuel oxygen content reduces particle mass and particle number in the accumulation mode; however, they increase the proportion of oxygenated organic species. PCA results for particle chemical and physical characteristics show that soot particles reactivity increases with fuel oxygen content.

1. Introduction The complex nature of compression ignition (CI) engine emissions has been a concern for decades owing to adverse health effects on humans and consequences to the environment [1–3]. Combustion in a compression-ignition (CI) engine produces particles, gaseous compounds, and vapour phase compounds that may adsorb and/or condense onto particle surfaces upon cooling in the exhaust system and during dilution [4,5]. Governments strictly regulate these emissions with increasingly stringent rules, representing a real challenge to automotive original equipment manufacturers (OEMs) [6,7]. In recent years, biodiesel has received significant attention as an

alternative fuel due to the growing energy demand and decreasing fossil fuel energy resources and rising concerns over environmental and health issues [8,9]. Some countries have passed legislation to use blends of biofuels and fossil fuel in the near future [10]. For example, in the transport sector, the legislators in Europe require the use of blends containing 10% biofuel by 2020 and United States requires the utilisation of 25%-biofuel blends [10]. These fuels have the potential to be utilised directly in internal combustion engines or blended with fossil fuels [11,12]. Biodiesel and biodiesel blends lead to a fuel product with different physical and chemical properties compared with that of conventional diesel [13]. Biodiesel has an oxygen group in its chemical bonds that has been shown to be a dominant factor in reducing the

⁎ Corresponding author at: International Laboratory of Air Quality and Health (ILAQH), Queensland University of Technology, Brisbane, Queensland 4000, Australia. E-mail address: [email protected] (Z.D. Ristovski).

https://doi.org/10.1016/j.enconman.2019.112183 Received 2 August 2019; Received in revised form 11 October 2019; Accepted 12 October 2019 0196-8904/ © 2019 Elsevier Ltd. All rights reserved.

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strong influence from the exhaust temperature on polycyclic aromatic hydrocarbons (PAH) and particulate emissions. Popovicheva et al. [27] studied a heavy-duty CI engine operated in steady-state and transient conditions while it was fuelled by diesel, 30% biodiesel blended with diesel and neat biodiesel. They applied PCA to study the exhaust particulate chemical composition. PCA results indicated the potential impact of biodiesel blending on the chemical characteristics of CI engine exhaust emissions. Biodiesel particulate had more hydrogen-rich carbon fragments as well as oxygen fragments. On the other hand, diesel particulate were corresponded to low-hydrogenated hydrocarbons. Rocha and Corrêa [28] studied the sources of metallic elements in diesel particulate matter emissions by trucks and buses fuelled by blends containing 5% to 20% biodiesel. They used PCA to group the metal compounds based on engine speed and fuel type. They found a strong effect of engine speed on metallic elements emissions. Also, they showed a reduction in the exhaust emission of metallic elements except lead by blending diesel with biodiesel. This study focuses on using correlation analysis, PCA and hierarchical clustering. Fuels with a range of fuel oxygen content were used to study the effect of oxygen in CI engines. These included diesel, several diesel–biodiesel blends and biodiesel blended with low volumes of triacetin, a highly oxygenated additive [14,29–32]. While most other studies focused just on a few engine performance or emission parameters, this study considers a wide range of parameters from six main group: engine performance, in-cylinder derived data, structure-borne acoustic emission (AE), fuel properties, exhaust emission, and particle morphology and nanostructure. AE was used to investigate the feasibility of such sensors to monitor the engine performance and emissions. The engine emission was focused on NOx and particulate matter. A noted advantage of this investigation is to study engine particle emissions in detail considering the chemical composition and physical properties of particles. This provides useful information on oxidation reactivity of particles using oxygenated fuel, which is important in improving the efficiency of diesel after-treatment systems [33]. This study presents a comprehensive understanding of the relationships between forty engine parameters which could benefit automobile industries for adapting oxygenated fuel to CI engine.

exhaust emissions [14]. The higher oxygen content of biodiesel leads to a reduction in particulate mass (PM); however, it raises nitrogen oxides emissions [15]. Also, biodiesel has a smaller greenhouse gas emission footprint since it is derived from renewable resources [15]. Hence, investigating the engine and exhaust emission behaviour of such biofuels is vital to assess their suitability for further utilisation. Engine performance, fuel parameters, exhaust emissions and their characteristics need to be simultaneously considered to obtain a comprehensive insight into pollution formation in the engine [6]. This can be achieved by utilising an effective multivariate analysis to identify the underlying factors that affect the engine performance and exhaust emission [16]. In the area of CI engine research, a range of multivariate techniques have been used for different purposes, for example principal component analysis (PCA) [17], preference ranking organization method for enrichment evaluations and geometrical analysis for interactive aid (PROMETHEE-GAIA) [18], analysis of variance (ANOVA) [19], and multiple regressions analysis [20]. Among these methods, PCA is more favourable as it simplifies a complex set of data by reducing the dimensions of the original data into a smaller sets of dimensions (principal components) containing the most information of the original data set [21]. Principal component analysis (PCA) is a widespread multivariate analysis that has been used in many fields [22]. PCA is a dimension reduction method which represents the interrelated data based on the largest variations or principal components [23]. It was mainly used in chemometric studies to reveal the correlation, anti-correlation or complete lack of correlation of the parameters [24]. This method primarily keeps most of the original information while removing noise from data. If the first few principal components contain a high level of variance from the original data set, they can be used to find the correlations between parameters [21,25]. Parameters that have similar behaviour will be grouped together. A few studies have used PCA for internal combustion engine research [17,26–28]; however, to the knowledge of authors, there is no literature investigating the correlation between extended engine exhaust emissions and performance parameters. McDonald et al. [26] conducted an interesting study using PCA and PLS to show the correlation between particles, semi-volatile organics species and their toxicity, determined by rat lung tissue damage and inflammation, and mutagenicity in bacteria. Exhaust emissions were sampled from both diesel and gasoline vehicles. Their analysis showed that the chemical species strongly vary with toxicity. Also, this correlation allowed the production of models that predicted the samples’ relative toxicity with an acceptable accuracy. Overall, they found that PCA analysis has the ability to extract the response associated with differences in composition, even if the exposure nature is complex. Clairotte et al. [17] performed experiments on two-stroke mopeds due to their health concern and pollutants emission. They used PCA to find correlations between different exhaust emissions such as carbon dioxide (CO2), nitrogen oxides (NOx), hydrocarbons, particle number (PN) and particle chemical compositions. The PCA analysis revealed a

2. Materials and methodology 2.1. Observations and parameters Six different fuels were chosen for this study arranged by oxygen content as shown in Table 1. Diesel (D) and coconut biodiesel (B) were used as base fuels and triacetin (T) was utilised as the oxygenated additive to biodiesel. Fuels were neat diesel, D80%-B20% (B20), D50%B50% (B50), neat biodiesel, B96%-T4% (B96T4) and B90%-T10% (B90T10), by volume. For each fuel, the combination of six loads and speeds were tested to illustrate different engine operating conditions. Thus, thirty-six combinations of fuel and engine condition were

Table 1 Fuel properties.

Density [kg/m3] Kinematic viscosity [cSt] Higher heating Value [MJ/kg] Lower heating value [MJ/kg] Cetane number Stoichiometric Air to Fuel ratio C [wt. %] H [wt. %] O [wt. %]

Diesel1

B20

B50

Biodiesel2

B96T4

B90T10

Triacetin3

837.90 2.64 44.79 41.77 53.30 14.66 86.35 13.65 0.00

842.32 3.08 43.81 40.86 54.36 14.22 84.30 13.35 2.36

848.95 3.73 42.35 39.49 55.95 13.56 81.26 12.89 5.84

860.00 4.82 39.9 37.20 58.60 12.49 76.31 12.15 11.54

872.00 4.94 39.03 36.38 56.86 12.15 75.89 11.85 12.26

890.00 5.12 37.72 35.16 54.24 11.66 73.82 11.41 14.77

1160.00 7.83 18.08 16.78 15.00 6.04 49.54 6.47 43.99

Providers: 1Caltex® Australia, 2Suncoast Renewables, 3Redox Pty Ltd. 2

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Table 2 Parameters used in multivariate analysis. Type

Parameter

Abbreviation

Unit

Engine performance

Speed Brake power Torque Fuel flow rate Injection pressure Exhaust temperature Boost pressure Brake thermal efficiency Air–fuel equivalence ratio Indicated work Indicated mean effective pressure Peak pressure Start of injection Start of combustion Ignition delay Maximum rate of pressure rise Signal maximum envelope Signal root mean square Brake specific fuel consumption Density Oxygen content Kinematic viscosity Brake specific CO2 Brake specific NOx Particle mass (1 µm or less in diameter) Particle number concentration Accumulation mode particle number concentration Accumulation mode count median diameter Nucleation mode particle number concentration Nucleation mode count median diameter Total organics Nitrates f44–Ratio of oxygenated organic marker to total organics f57–Ratio of hydro-carbon organic marker to total organics Primary particle diameter Fractal dimension Radius of gyration Fringe length Fringe tortuosity Fringe distance

Spd PwrB Trq qF InjP TExh Bst ηB λ IW IMEP PP SOI SOC IgnD RPRmax Envmax RMS BSFC ρF OxyR υF CO2 NOx PM PN PNA CMDA PNN CMDN Org NO3 f44 f57 Dp FD RGy fL fTr fDis

revolution per minute (rpm) kilowatt (kW) Newton-metre (Nm) litre per minute (l/min) Mega-Pascal (MPa) degree Celsius (°C) kilo-Pascal (kPa) % – kilo-Joule (kJ) kPa kPa degree of crank angle (°ca) °ca °ca kPa/°ca Volt (V) V gram per kilowatt hour (g/kWh) kilogram per litre (kg/l) % centistokes (cSt) g/kWh g/kWh g/kWh number per kWh (#/kWh) #/kWh nanometre (nm) #/kWh nm g/kWh g/kWh – – nm – nm nm – nm

In-cylinder derived data

Acoustic emission Fuel properties

Exhaust emissions

Particle morphology and nanostructure

to aerosol with diameters above 100 nm and therefore best represents the composition of accumulation mode aerosol [39]. The physical properties of particles are found from TEM images post processed by an in-house image processing technique [40]. These data are taken from the published study by Verma et al. [41]. Where necessary, the emission data is reported in the standard unit of grams per kilowatt hour. In total, forty parameters are considered in this investigation, as shown in Table 2.

investigated. All the fuel blends were made from the same batch. The engine inlet temperature and the temperature of the test cell were remained constant during all the experiments. Before each experiment, the engine was run for at least one hour to make sure that it is fully warmed up. Then, the experiments started in the following order: 75% load at 1500 rpm, 50% load at 1500 rpm, 25% load at 1500 rpm, 100% load at 1500 rpm, 100% load at 1800 rpm and 100% load at 2000 rpm. The parameters under investigation are shown in Table 2. The parameters are categorised into six groups: engine performance, incylinder derived data, structural-borne acoustic emission, fuel properties, exhaust emissions, and physical properties of particle emission. Incylinder pressure, along with crank angle and injector data, were used to determine different combustion parameters such as ignition delay and rate of pressure rise [34,35]. For this study, 3000 cycles were acquired at each experimental condition and their average values were considered in the analysis. Structural-borne acoustic emission was acquired using an acoustic emission sensor. It can acquire high frequency pressure waves emanating from the engine block. In this study, two characteristics of the signal were selected: the root mean square and maximum envelope of the signal. The former represents the power of the signal and the latter is an indication of the maximum amplitude of the signal. The envelope AE signal is determined by Hilbert transform of the signal [36,37]. For the exhaust emissions, two important CI engine emissions are investigated here: NOx and particles. The chemical composition and physical properties of particles are investigated in detail. The chemical composition is determined by an AMS [38]. The AMS is most sensitive

2.2. Experimental setup This investigation was carried out in the Biofuel Engine Research Facility (BERF) at the Queensland University of Technology (QUT) employing a common-rail, turbocharged, after-cooled, six-cylinder diesel engine (Cummins ISBe220-31) [32,42]. The engine brake power is absorbed by an electronically controlled water-brake dynamometer. The experimental schematic diagram is shown in Fig. 1 and an overview of the facility follows. An in-cylinder pressure transducer was installed in the first cylinder of this research engine (Kistler piezoelectric transducer type6053CC60). The crank angle was measured by a rotary encoder (Kistler type-2614) with a resolution of 0.5 degrees [42]. Structural-borne acoustic emission was acquired using a general purpose acoustic emission (AE) piezoelectric sensor (Physical Acoustics R15α). The AE sensor was mounted on the engine block close to the first cylinder where the pressure transducer was installed [36]. These data were acquired synchronously using two National Instrument data acquisition 3

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Fig. 1. Experimental Setup.

boards (NI-9223 synchronised by a NI-9250) utilising LabVIEW® software. The acquisition data rate in this study was one million samples per second to satisfy the Nyquist sampling theorem for the full frequency band of the AE sensor [43]. Other sensors were also installed on the engine to record exhaust temperature, charged air flow, fuel consumption, boost pressure, common-rail pressure, engine speed and torque. Two California Analytical Instruments (CAI) series 600 – non-dispersive infrared (NDIR) and chemiluminescence detector (CLD) – were used to measure the CO2 and NOx in the exhaust, respectively. The NDIR was directly connected to the exhaust. A dilution tunnel was used to dilute the engine exhaust with filtered zero air. In order to obtain the dilution ratio, a SABLE (CA-10) was utilised to measure CO2 after the dilution tunnel. CAI CLD and nanometre aerosol sampler (NAS–TSI 3089) were used after the dilution tunnel. The NAS captured and collected particles on holey carbon grids for morphological and nanostructural analysis by transmission electron microscopy (TEM) [44]. A fast particulate analyser, DMS500, was used to measure the particle number size distribution of the exhaust from which the size distribution can be integrated to obtain particle number concentration. An Aerodyne aerosol mass spectrometer (AMS–Aerodyne Research Incorporation) was utilised to analyse the chemical composition of non-refractory exhaust particles [45]. Since the AMS is mainly designed for sampling at the atmospheric concentration level, a two-stage dilution –consisting of a dilution tunnel and a DEKATI diluter (D, mI-1000)– was utilised. The DMS500 took samples at the same point as the AMS to have comparable measurements from these two instruments.

corr (Vn, Vm) =

cov (Vn, Vm) σVn σVm

(1)

where Vs are the parameters, m and n are the number of parameters and cov is the covariance. When a large number of parameters are available, the correlation coefficient produces a large matrix of relationships between each two parameter combinations. Using a dimensionality reduction method, such as PCA, can make the interpretation of data more effective. A range of mathematical methods are available to find the principal components of a set of observations [21]. One of the basic and wellknown methods is singular value decomposition (SVD). SVD is a class of Eigen value problems in mathematics and is computationally efficient. This method is applicable when there are no missing values in the data matrix. As shown in Eq. (2), it decomposes matrix X into two sets of orthogonal matrices – Y and U – and one diagonal matrix, Σ [21,48]. SVD can be represented as: (2)

X = YΣU'

where Y is left singular vectors matrix, U is right singular vectors matrix and is known as a loading matrix. Σ is a diagonal matrix and contains the eigenvalues –or explained– of XXT. The major advantage of SVD is that it can decompose X in one operation without the need to determine a covariance matrix [21]. However, if the parameters have different units, they need to be scaled to a standardized to unit variance. One of the common methods of scaling the data is the standardised z-score. This method transforms the mean of the data to zero and the standard deviation to one while keeping the data distribution unchanged. It is an acceptable pre-processing method of PCA and suggested by Bro and Smilde [49].

2.3. Data analysis techniques

z i, j =

A major aim of this study is to find the relationship between parameters shown in Table 2. Pearson’s correlation of these parameters, principle component analysis and hierarchical clustering are used to explore these relations. All the analysis in this study was performed in MATLAB®. The data that was used in this study can be found in Appendix A. The correlations between parameters are determined by Pearson’s correlation coefficient [46], Eq. (1). It indicates the non-dimensional linear dependency of two parameters with the same number of observations. In other words, the correlation coefficient explores the variation of two parameters by scaling the covariance over the standard deviation [47]. Thus, this can be a straightforward and useful method in engine research where parameters of various units exist.

(x i, j − μ j ) σj

(3)

where z is the transformed parameter corresponding to x, μ is the mean and σ is the standard deviation of the parameter. i indicates the number of observation and j shows the number of parameter. While PCA can cluster parameters that correlate with each other, one may find it hard to show the PCA result in three dimensions. Thus, an unsupervised clustering method is used in this study to show the clusters. Hierarchical clustering identifies groups of parameters which have minimum distance from each other. Initially, it considers each parameters as one cluster and, then, merges the clusters together with the minimum distance from each other. It continues to link the clusters together until one cluster remains [50]. This method is used as a 4

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Fig. 2. Analysis diagram of this study.

complement to PCA in order to demonstrate the result more clearly. Fig. 2 illustrates the analysis diagram of the current study. Most experiments are subject to errors and uncertainties. In engine research, uncertainties in the experimental results are mainly due to experiment procedures, measurement equipment calibration and condition, and fuel selection [51]. To minimize the uncertainties associated with the experiment procedure, the same procedure was applied to all the fuels. A day before testing each fuel, the engine fuel line was cleaned and the new fuel was pumped through the line. Then, the engine was run for one hour to make sure that the previous fuel was completely replaced by the new fuel in the engine. On the day of the experiment, the engine was started and warmed up for an hour or until the engine oil and water temperature reached 90 °C, and, then each test was performed. All instrumentation was serviced and calibrated before the experimental campaign. Since fuel properties can vary between each batch of fuel, the same batch of diesel, biodiesel and triacetin was used throughout the experimental campaign. Uncertainty arises in PCA when the considered number of principal components (PCs) do not adequately explain the variance of the whole data set or the number of observation is small compared to the number of parameters [52]. Different methods have been proposed in the literature to select the number of meaningful principal components such as “scree test” or “proportion of variance accounted for” [53]. In the scree test, PCs are chosen if they have relatively larger eigenvalues than the remaining PCs [54]. With the other method, a subset of PCs are selected which represent at least 70% of the total variance in the data, while individually accounting for a minimum specified percentage – e.g. 5% or 10. In this study, the first three principal components were chosen that explain 79.2% of the total variance of the dataset, satisfying both of the criteria above as shown in Fig. 3. Furthermore, using these PCs, the data can be interpreted in a meaningful way [55]. While more parameters than samples are considered here, PCA is still valid and can be employed as the total variance of first three PCs are large enough compared to the others [56]. Thus, they can be used to explore the relationship between the parameters–using loadings plot.

Fig. 3. Eigenvalues associated with principal components on left and cumulative variance explained by PCs on right.

equivalence ratio (λ). On a closer inspection, brake specific CO2 is anticorrelated with the former engine performance parameter and is correlated with λ. Therefore, CO2 and λ can form a group which is anti–correlated with brake power and torque. It can be concluded at higher load, the availability of air decreases which can result in a reduction of brake specific CO2 and possibly an increase of other emissions such as hydrocarbon and/or carbon monoxide. While the cluster of engine performance parameters is clear, it may be difficult to identify and classify other variables. For example, fractal dimension (FD) is anti–correlated with engine performance parameters, similar to CO2 and λ. However, FD shows correlation with fuel parameters such as oxygen ratio while CO2 and λ do not show significant correlation with them. In this case, FD can be affected by different parameters. PCA can facilitate the interpretation of the result by decreasing the dimensions of the data and emphasising the largest variation as shown by loadings plot in Fig. 5. Fig. 5 defines the linear relationship between the parameters presented by each particular principal component. The parameters that lie near each other vary together. The parameters located opposite each other are anti–correlated. The parameters that are neither correlated nor anti-correlated do not depend on each other. In this case, they are located in the range 45 to 135 degrees of each other in a plane of two principal components. As discussed in the methodology, the clusters are determined by hierarchical clustering using the loading value of the first three principal components. The dendrogram of the latter analysis is presented in Fig. 6. In this figure, the horizontal axis shows the parameters and cluster while the vertical axis represents the distance –in this case Euclidean distance– based on the first three PCs [57]. The longer the vertical line, the more dissimilar the two parameters or clusters are. Although this technique clusters the parameters, it cannot set the number of clusters. The number of clusters was chosen in order to have minimum significant clusters which indicate more similarity – i.e. a shorter vertical axis. The dark red cluster (A) on the right side of Fig. 5 presents most of the engine performance data together with AE indicators and some of the in-cylinder parameters such as peak pressure and indicated mean effective pressure. This cluster is anti-correlated with the orange cluster (C). Cluster C includes brake specific carbon dioxide (CO2), air–fuel equivalence ratio (λ), fringe tortuosity (fTr) and fringe distance (fDis). Cluster H primarily represents the parameters correlated with brake specific particle mass (PM), such as organics (Org), the accumulation mode count median diameter (CMDA), the nucleation mode count median diameter (CMDN) and radius of gyration (RGy). The latter

3. Results and discussion The correlation matrix of all parameters is shown in Fig. 4. This graph reveals the correlation of each pair of parameters. The blue colour indicates correlation of two parameters, while the red colour shows anti-correlation and grey indicates no correlation. The size of the circles depicts the magnitude of the pairwise correlation coefficient. The diagonal element is simply one, since it expresses the correlation of the parameter with itself. The correlation matrix is informative in the sense that it shows the correlation of each pair of parameters. For example, some engine performance parameters such as brake power, torque, injection pressure, and exhaust temperature have positive correlation together as shown in the top left of Fig. 4, while they are anti-correlated with air–fuel 5

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Engine performance

In-cylinder derived data Acoustic emission Fuel properties

Exhaust emissions

Particle morphology and nanostructure

Fig. 4. Correlation matrix illustration.

G

SOI S SOC

RPR R RP Rm maxx DP

PC 3 - 15.14%

NOX NO

F

ff44 f4 44 4 4

fL

T Exh Ex E xh

E FD

CMD CM M MD DA N O3 C NO

F

OxyR Oxy F

BSFC BSF B BS SF SFC S FC

Cluster F includes the maximum rate of pressure rise (RPRMax), primary particle diameter (Dp) and fringe length (fL). This cluster also shows anti-correlation with cluster E especially in the PC1–PC2 plane. Cluster D contains particle number concentration (PN), the accumulation mode particle number concentration (PNA) and the ratio of hydro-carbon organic marker to total organics (f57) and is anti-correlated with E and B. An overall interpretation of Fig. 5 is given in Table 3 and Table 4. Table 3 shows the inter-correlation of parameters in each cluster. Table 4 shows the correlation and anti–correlation of pairs of clusters and describes the main interpretation. A detailed explanation of the important parameters is presented in follow. All engine performance variables are grouped together except speed and air–fuel equivalence ratio (λ) in Fig. 5. While the engine load is increasing, it uses more fuel and supplies higher power and torque. The brake thermal efficiency (ηB) of the tested engine increases with engine load [67]. The relationships between engine performance parameters are consistent with generally accepted diesel engine performance parameters [58,68–70]. λ is anti-correlated with engine variables, which means that the availability of air decreases at high engine loads. Structural-borne acoustic emission (AE) indicators, NOx, FD, fL, brake specific fuel consumption (BSFC) and in-cylinder pressure parameters are other parameters that are correlated or anti-correlated with engine performance. These parameters will be discussed in more details in below. The fuel properties parameters considered in this study are fuel density (ρF), oxygen weight ratio (OxyR), kinematic viscosity (υK). These four variables make a group with NOx, FD and f44 in Fig. 5. Biodiesel and triacetin have higher ρF, υK and OxyR, and, hence, these values are grouped together [71,72]. The positive correlation of BSFC variation with other fuel parameters show that by increasing fuel oxygen content, fuel consumption increases [14,73]. Also, the lower heating value (LHV) of the biodiesel and triacetin are less than diesel which leads to higher BSFC and lower engine power [74,75].

CMD CM C M N MD

Org O rg rg T

B

RGy Gy

H PM PM

A

Trq Trq q W IIMEP IM MEP ME EPIW IInj P qF

Pwr wr wr Env E nvvP ma max axx B a Bs Bst B st PP P RMS RM R MS S

CO2 CO PN A PN

C fTTr fDis

ff57 f5 57 7

D

PN P NN PN PN

B

Spd

Ign IIg gn D gn

PC 2 - 25.17% PC 1 - 38.89%

Fig. 5. Loadings Plot of the first three principal components.

cluster inversely varies with the green cluster (E). This cluster contains fuel properties as well as brake specific nitrogen oxides (NOx), fractal dimension (FD) and the ratio of oxygenated organic marker to total organics (f44). The purple cluster (B) in the lower centre includes Speed, the nucleation mode particle number concentration (PNN) and ignition delay (IgnD). This cluster is anti-correlated with two clusters shown in light blue (F) and dark blue (G). Group G is formed by just two parameters, start-of-injection (SOI) and start-of-combustion (SOC). 6

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Fig. 6. Dendrogram of hierarchical clustering based on the first three principal components. Table 3 General interpretation of parameters in each cluster. Cluster

Parameters

Interpretation

Significance

A

PwrB, Trq, qF, InjP, TExh, Bst, ηB, IW, IMEP, PP, Envmax, RMS

The engine performance parameters are consistent with literature [58]. An elaborate signal processing tool can be employed to reconstruct in-cylinder pressure using acoustic emission sensor.

B

Spd, IgnD, PNN

More fuel and supplies higher power and torque. IW, IMEP, and PP individually represent the engine performance. The injection of more fuel in to the cylinder results in higher PP and a stronger acoustic signal propagates in the engine structure. At higher speed, IgnD is longer due to advanced SOI.

C

λ, CO2, fTr, fDist

Higher λ increases particles fringe distance and tortuosity.

D

PN, PNA, f57

The variation in PN is mostly derived by PNA. f57 contributes to the larger particles.

E

NOX, f44, FD, BSFC, ρF, OxyR, υF

F

RPRmax, DP, fL

Oxygenated fuel can increase NOx, f44 and FD. The positive correlation of BSFC variation with other fuel parameters show that by increasing fuel oxygen content, fuel consumption increases [14]. Higher RPRmax causes a rapid increase in temperature during combustion, which increases DP and fL [63].

G H

SOI, SOC PM, CMDA, CMDN, Org, NO3, RGy

Start-of-combustion varies with start-of-injection. Increase in CMDs indicate larger particles and contributes to larger PM and RGy. When PM is larger, there are more Org and NO3 available.

Longer ignition delay and higher engine speeds can increase the nucleation particle emissions. Higher λ can increase the oxidation potential of the soot by increasing fringe distance and tortuosity [59]. Correlation of f57 with PNA shows that the hydrocarbons are condensed more on larger particles that brings them into the measurement range of the AMS. NOx increasing trend is consistent with literature [4,60,61]. Oxygenated fuel particle emission are more reactive [38]. Oxygenated fuel produces more compact particles [41,62]. Initial stage of combustion affects Dp and fL [64]. Note that this observation has been made in diffusion flame studies and has been shown here for CI engine [65]. SOI influence on SOC agrees with expectation [34]. PM –measured by DMS500- and RGy – measured by TEM- are proportional to AMS mass basis measurement – organic and nitrate [66].

Table 4 The relationship between pairs of clusters. Pair

Cluster

Correlation

PCs

I

A, C

Anti-correlated

1, 3

II III IV V

A, H B, D B, H B, G

Uncorrelated Correlated Anti-correlated Anti- correlated

1,2 1,3 2,3 1,2,3

Comment is anti-correlated with engine parameters, which means that the availability of air decreases at high engine loads. This results in • λmore brake specific CO . reduction in particle mass (H) is dominated by oxygen content of fuel rather than engine performance parameters (A). • The speed and longer ignition delay, the engine produces more PN in total. • AtThishigher engine produces less PM at higher speed and longer Ign . • At higher the engine control unit advances SOI, as a result SOC. This causes to stretch Ign . • Advanced speed, SOI results in less PN . • Air-fuel equivalence ratio is higher for oxygenated fuel, due to oxygen content. • Brake specific fuel consumption • Smaller λ can result in bigger D is. proportional to CO . • By decreasing fringe length (F ), fringe tortuosity and fringe distance increases, which make the particle more disordered. • PN has major contribution to PM. • Oxygenated fuel produce less particle number in the accumulation mode. • Hydro-carbon organics decrease with increase in the oxygen content of fuel. • The increase in PN combined with a smaller fractal dimension (F ) gives a larger surface area for the condensation of higher volatility • hydro carbons (f57). fuel produces less PM and, hence, less Org and NO . • Oxygenated of accumulation and nucleation modes decreases by increase of fuel oxygen content. • CMDs • Start of injection (G) is controlled by engine control unit, and the fuel type does not have an influence. 2

D

D

N

VI

C, E

Correlated

1,2

VII

C, F

Anti-correlated

1, 3

VIII IX

D, H D, E

Correlated Anti-correlated

1,2 1,2,3

2

p

L

A

A

X

E, H

Anti-Correlated

1,2

XI

E, G

Uncorrelated

1,2,3

D

3

the engine performance [76]. SOI and SOC make a separate cluster which is anti–correlated with speed and ignition delay IgnD. At higher speed, the engine management system advances SOI, as a result, also advances SOC. This can lead to longer IgnD, as well [77].

The in-cylinder parameters are determined using cylinder pressure trace and crank angle [34,35]. In Fig. 5, indicated work (IW), indicated mean effective pressure (IMEP) and peak pressure (PP) are in a group with the engine performance parameters as they individually represent

7

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Higher temperature can increase the soot oxidation process and result in the PNN growth. In Fig. 4, PNN has a correlation with fuel properties such as fuel oxygen content. It indicates that biodiesel can generally increase ultrafine particle emissions [93]. The relationship between particle chemical composition and fuel properties shows that OxyR, ρF, and ʋF affect the chemical composition of compounds condensed on particles [38]. In Fig. 5, Org, NO3 and f57 are anti-correlated with fuel parameters, and f44 is grouped with fuel parameters. Oxygenated fuel lead to formation more oxygenated species on particles [45]. This is shown by the increase of f44 which represents the oxygenated organic aerosol [38]. In Fig. 4, f44 is anticorrelated with speed which may indicate that by decreasing the combustion duration the oxygenated organic aerosol decreases. f57 is clustered with PN and PNA which indicates that the ratio of hydrocarbons in exhaust is increasing when PN increases. Correlation of f57 with PNA shows that the hydrocarbons are condensed more on larger particles that brings them into the measurement range of the AMS [39]. Dp is grouped with RPRmax and fL in Fig. 5. It illustrates that the initial stage of combustion affects Dp and fL. According to Fig. 4, the variation of Dp is proportional to RPRmax. Higher RPRmax lead to rapid rate of fuel burning [94]. Thus, more fuel is probably trapped in the middle of the diffusion flame, where the temperature is high and there is not enough oxygen [95]. This can boost the pyrolysis and nuclei process as a precursor to create larger Dp [63]. Dp is anti-correlated with OxyR which indicates that oxygenated fuel can provide more oxygen in the diffusion flame and may reduce the pyrolysis of the fuel. While the latter is true, the formation of the Dp is not only dependent on the available oxygen as it is also related to the initial stage of the combustion [65]. Fig. 5 shows that FD generally increases with the increase of fuel oxygen content or, in other words, fuel oxygen content can produce more compact particles [41,62]. FD is correlated with λ in Fig. 4. This can be due to more availability of charged air in the cylinder that results in the more compact soot aggregates [96]. Also, FD is anti-correlated with engine parameters, mainly PP and the exhaust temperature (TExh). At higher pressure and temperature, coagulation and agglomeration of particle clusters increase that result in chain-like structure of particles and smaller FD [41,63]. No correlation is found with speed and initial combustion parameters since soot aggregates form in the second stage of combustion in the chamber and, later, in the exhaust [63]. RGy is in the same group with PM in Fig. 5. This shows that the larger the particles, the larger the radius of gyration. Also, RGy inversely varies with fuel properties. Fringe length (fL) is grouped with RPRmax and Dp. By an increment of RPRmax, the fringe length can increase while it is anti-correlated with speed and ignition delay. fL is also increasing with pressure which agrees with literature [97]. On the other hand, it is anti-correlated with fTr and fDis. Jiang et al. [98] and Hirner et al. [65] observed the same relationship between fL, fTr and fDis. These relationships can show that by increasing both pressure and temperature in the premixed combustion phase, fL can increase and, hence, results in oxidation resistance of exhaust nanoparticles emissions [97]. fDis and fTr are grouped with equivalence ratio and CO2, and is anticorrelated with engine parameters in Fig. 5. When the equivalence ratio is higher and more air is available, the particles have higher fringe distance and tortuosity that result in higher oxidative potential of particles [99]. Fig. 4 shows the dependency of fDis on the fuel properties and increases with fuel oxygen content that shows higher oxidative potential of particles produced by oxygenated fuel. In Fig. 4, both fDis and fTr are anti-correlated with RPRmax and correlated with speed. These indicate that the structural distorted in particles decreases at lower speed and higher RPRmax, hence higher temperature [100]. This study shows that the fuel oxygen content increases the reactivity of soot particles as evident in particle chemical and physical characteristics: f44, fL, fDis, and fTr [41,45]. Also, oxygenated fuels produce more compact and smaller particles as evident in FD, CMDs, and Dp. These characteristics of particle emissions from oxygenated

RPRmax is positively proportional to SOI and SOC, and anti-correlated to IgnD and speed. Fig. 4 shows not only engine load but also engine speed can have an effect on RPRmax. At Lower engine speed, more time is available for fuel to combust that results in the higher RPRmax. Longer residence time and higher RPRmax cause higher in-cylinder temperature that increases the formation of NOx [78,79]. On the other hand, shorter residence time and lower RPRmax can lead to less complete combustion [80]. Also, there is less time for agglomeration of soot, and, hence, the particle number may increase. Root mean square (RMS) and maximum envelope (Envmax) of AE signal are used in this study to investigate the relationship between these two parameters and the engine performance data, especially incylinder peak pressure (PP). As shown in Fig. 5, RMS and Envmax remain in the same cluster as PP. RMS, Envmax and PP have a large contribution to build PC1 along with other engine performance parameters. Based on this and Fig. 4, it can be concluded that the variation of the AE signal is highly correlated with the variation of peak pressure. When PP is higher, a stronger acoustic signal propagates in the engine structure. Here it is shown that the AE sensor is capable of acquiring a signal that can be utilised to monitor the engine performance and, possibly, combustion. The information provided by PCA can be used to construct an empirical model using some statistical modelling tools such as principal component regression (PCR) or partial least square regression (PLS) [81,82]. The model can be used to estimate the engine performance and pollution parameters that are correlated or anti-correlated with RMS or Envmax such as the engine power or NOx. This model can be physically justified –yet empirically fitted–, since the in-cylinder parameters affect NOx emission and AE strongly correlates with PP, IW and IMEP. AE indicators are capable of reconstructing the in-cylinder pressure signal [83]. The number of principal components are important for making a reliable model. The number of More elaborate signal processing tools such as autoregressive moving average (ARMA) or neural network (NN), can be employed to reconstruct in-cylinder pressure using acoustic emission sensors [84]. Based on the first three principal components, brake specific nitrogen oxides (NOx) is grouped with fuel parameters, fractal dimension and f44. NOx shows an increasing trend in the presence of fuel oxygen content, which is consistent with literature [4,60,85]. Based Fig. 4, NOx is anti-correlated with the engine performance parameters such as brake power and torque and correlated with λ. Also, Fig. 4 reveals the dependency of NOx to RPRmax, IgnD and speed. At lower speed, NOx increases, as it is time available for NOx formation [86]. RPRmax is proportional to the instantaneous temperature and, as a consequence, to NOx [61,87]. Furthermore, NOx shows correlation with FD and f44 in Fig. 4, since all these parameters are correlated with the fuel oxygen content. PM is located in cluster H, where it has a significant contribution to PC2 in Fig. 5. PM – measured by DMS500– and RGy – measured by TEM – are proportional to AMS mass basis measurement–Org and nitrate NO3 [66]. Increase in CMDs indicate larger particles which contribute to larger PM and RGy [88]. All these parameters are anti-correlated with fuel properties as shown in Fig. 4 and Fig. 5. It has been shown in the literature that the oxygenated fuel can decrease PM [31,73,89,90]. PM mainly varies with the fuel parameters while it shows insignificant correlation with the engine performance parameters. PM is correlated with particle number in the accumulation mode in Fig. 4. This indicates that particles in the accumulation mode are the dominant contributor to the particulate mass. PN is grouped with PNA and f57 in Fig. 5. PN is determined by accumulation PNA and nucleation PNN. Therefore, PN can be expected to be located between PNA and PNN. The variation of PN is mostly derived from PNA since they are grouped together [91]. Fig. 4 reveals that the PN increases at higher speed, where there is shorter combustion duration [92]. PNN is located in group B with speed and IgnD. Longer IgnD delays combustion and lead to high gas temperature [74]. 8

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acknowledge: Mr. Andrew Elder from DynoLog Dynamometer, Dr. Doug Stuart from Suncoast Renewables for providing coconut biodiesel, and CALTEX Australia for providing diesel. The support from Australian Research Council (ARC) under discovery grant program DP180102632 is also acknowledged.

fuels can potentially increase the health risk, despite reducing PM [45,101]. A proper after-treatment device, consisting of a diesel oxidation catalyst (DOC) and diesel particulate filter (DPF), can be employed to remove oxygenated fuels particle emissions provided that the efficacy of the DPF can be improved by modifying the permeability and porosity of its filter channels [33,102].

Appendix A. Supplementary data 4. Conclusion Supplementary data to this article can be found online at https:// doi.org/10.1016/j.enconman.2019.112183.

Principal component analysis has been used to identify the relationship between regulated and unregulated exhaust emission, engine operating conditions and fuel characteristics of CI engine fuelled by diesel and biodiesel with oxygenated additive. These findings provided more insight into pollution formation in the engine. Results showed:

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• Maximum rate of pressure rise was correlated with brake specific • • • • • • • •

NOx and anti-correlated with total particle number regardless of the fuel type. Both fDis and fTr increased with shorter combustion duration. fDis showed dependency on the fuel properties and increased with oxygen content. fL can increase by RPRmax and maximum in-cylinder pressure while it was anti-correlated with combustion duration. Formation of the Dp was dependent on the fuel oxygen content and the initial stage of the combustion. Soot aggregate compactness increased by the availability of oxygen in the cylinder, from the intake air or fuel. Also, soot aggregate compactness decreased as peak pressure and exhaust temperature increased. Oxygenated fuel produced smaller particles as evident in count median diameter. Fuel oxygen content increased the reactivity of soot particles as evident in f44, fL, fDis, and fTr. Structure-borne acoustic emission correlated with in-cylinder peak pressure and engine parameters, while it was anti-correlated with brake specific NOx.

This study used only coconut oil biodiesel; however, engine performance and exhaust emissions using biodiesel from different feedstocks can be studied by using the same analysis technique as has been presented here. This can show the difference in performance of different type of biodiesels. Depending on the chosen parameters, the variance, that each PCs explains, will be different. More PCs can be considered as long as it satisfies a criteria for selecting PCs such as the scree test. This study also showed that AE sensors can be employed as a diagnostic tool to monitor engine performance and, hence, emissions. Future work could include an empirical or semi-empirical model of the system based on the statistical and physical relation between AE indicators, in-cylinder parameters and emission such as NOx. The dimensionally reduced data set generated by PCA can be utilised to build a model using statistical modelling tools, such as principal component regression. Furthermore, other fuel types and engine operating modes should be considered to develop a more comprehensive model. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgment The first author would like to acknowledge QUT for providing Ph.D. scholarship (QUTPRA). The authors would like to acknowledge Mr. Noel Hartnett for his invaluable technical knowledge and assisting with the experimental campaigns. The author would also like to 9

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