1

Estimation of Particulate Matter from Smoke, Oil consumption and Fuel Sulphur

P.A. Lakshminarayanan and S. Aswin

Simpson and Company Ltd., Chennai

Particulates from diesel engine consisting of particles of carbon, sulphates, oil, fuel and water are

measured by filtering a sample diluted in a partial or full flow tunnel according to strict standards and

weighing them. However, these methods suffer from high initial and running costs. On the other hand,

filter smoke meters measure the light reflected from a filter paper through which a known volume of

exhaust gas is passed and Opacity meters measure light absorbed by a standard column of exhaust.

They measure visible black smoke easily at reasonable expenditure. Today, these simple instruments

are highly developed to control measurement noise, resolution and repeatability, and can be used to

estimate carbon soot precisely. Adding contribution of oil and sulphates from fuel helps in estimating

the Particulate Matter measured in ISO 8178 R49, D2 and C1 as well as ESC cycles up to EUStage IIIA,

India CEV Stage IIIA, Euro III (India BS III on-road) and India CPCB2 emission standards. In this paper, a

method is given to predict PM from fuel properties, engine oil consumption and measured smoke. The

estimated values of PM compared favourably with the estimated values with confidence margins

enough to go for type testing.

1 Introduction Particulate matter (PM) from a diesel engine is multifaceted. Samples are taken from a tunnel at a

temperature of 47°C ± 5°C where the exhaust gases are diluted [1] in the range 3:1 - 20:1 to achieve

the desired cooling effect. The gas samples are filtered and weighed using a balance. Not only the

infrastructure and the equipment are initially expensive, but also are maintenance intensive with high

running costs. The particulate matter consists of solids like ash, sulphates and carbon, and liquids like

heavy hydrocarbons derived from fuel and oil condenses during the dilution process. Diesel

particulates are of diameters below 0.04 μm and their agglomerates of diameters up to 1 μm. Efforts

have been made for many years to correlate smoke and other contents of PM with the total PM [2].

Majewski and Jääskeläinen [3] bemoan smoke opacity readings generally do not correlate well with

other PM measurement parameters and that numerous correlations between opacity or smoke

readings and PM mass that have been developed can provide only approximate results.

In this work, an attempt is made to find a working relationship for estimating PM from the sources of

sulphates, hydrocarbons, water vapour, as well as soot carbon.

2 Particulate Matter In regions of low air fuel ratio in the fuel sprays in a diesel combustion chamber, the evaporated fuel

forms carbon from intermediate products of reaction. The carbon in the form of soot burns

subsequently at the flame front however, insufficiently before the combustion gases leave the engine

2

cylinder. The carbon particles remaining in the torturous exhaust path starting from the exhaust port

to the silencer agglomerate, Figure 1 [4]. On these carbon particles unburned fuel and oil are

adsorbed. Added to this are sulphates originating from fuel sulphur. The particulate matter (PM) is

measured by sampling the gases in a full flow or partial flow tunnel. The full flow tunnel is very

expensive and the partial flow tunnel is not that cost effective both initially and to run during

development trials. The two methods are found to give equal results within experimental errors for

engines emitting PM between 0.05 and 0.4 g/kW h [5] and even for ultra low emission engines [6].

Figure 1 Schematic representation of diesel particulate matter (PM) formed during combustion of atomised fuel droplets. The resulting carbon cores agglomerate and adsorb species from the gas phase [4]

3 Composition of PM The PM [1] constitutes roughly 60% carbon, 5% ash from metallic additives in lubricating oil, 10%

sulphate from fuel sulphur, 20% lubricating oil and 5% liquid diesel, Figure 2. These correspond to fuel

containing about 350 ppm or less sulphur, common in Indian applications today.

Figure 2 Composition of PM Mass emissions from a conventional HD Diesel engine without DPF (SOF-soluble organic fraction) [1]

3.1 Sulphur in Fuel Sulphate is the dry sulphuric acid formed from the fuel sulphur. The intermediate product is sulphur

dioxide (SO2), a part of which forms less stable SO3 before forming the acid when it comes in contact

60%5%

10%

20%5% Carbon

Ash

Sulphate and Water

Lube oil SOF

Fuel SOF

3

with condensed water of combustion. The linear dependence of SO2 on fuel sulphur shows sulphate

component in the total particulate matter can be correlated with the known fuel sulphur, Figure 3 [7].

There is some sulphate contributed by the sulphur in oil, however. Similarly, in Figure 4, results of

experiments using specially prepared (natural gas) fuels in gas turbines with precisely added sulphur

compound (methyl mercaptan) after completely scrubbing the native sulphur show [8] that the

sulphates in PM are linearly related to the fuel sulphur.

Figure 3 Relationship of Sulphur dioxide, a precursor for sulphates in PM and sulphur in fuel [7]

Figure 4 Total particulate matter and sulphate in PM as a function of sulphur in fuel, Gas turbine experiments [8]

3.2 Hydrocarbon from Fuel and Lubricating Oil

3.2.1 Oil

Oil is consumed in diesel engines by the turbocharger and crankcase ventilation as well as past the

valve stem seals and cylinder system [9]. The cylinder system is normally the largest contributor to oil-

0.1

1.0

10.0

100.0

1,000.0

1 10 100 1,000 10,000

SO2,

pp

m

Sulphur Content in Fuel, ppm

BSFC =225 g/kWha/f = 20

Lub oil - 0.2% of fuel, S=10,0000ppm

Lub oil - 0.1% of fuel, S= 4,000 ppm

0

10

20

30

40

50

60

0 2 4 6 8 10

PM

(mic

rogr

am/m

3)

Injected Sulphur (ppm)

PM total

PM Sulphate

4

related particles and the in-cylinder oil consumption can be categorized as (a) throw-off, when oil is

driven up by inertia forces, (b) reverse blow-by driven upwards by pressure, (c) evaporation from hot

surfaces and (d) top-land scraping by carbon deposits on the top land (Figure 5).

Figure 5 In-cylinder oil consumption: (a) Throw off, (b) Reverse blow-by, (c) Evaporation and (d) Top land scraping [9]

3.2.2 Fuel

Diesel fuel has a wide boiling range. The highest condensates partly remaining unburned are observed

as hydrocarbons in the exhaust flow. A fraction of these hydrocarbons condense on the surfaces of the

soot particles on dilution at low temperatures.

3.3 Carbon Soot Polycyclic aromatic hydrocarbons (PAH) form and grow by hydrogen abstraction and acetylene

addition mechanism from the aromatic compounds of the fuel. On the nuclei, the gaseous PAHs

transition to solid particles forming nuclei of soot which grow adding mass by absorption of

hydrocarbons in vapour phase. Coagulation to form spherical particles and agglomeration to form

aggregate structures take place and result in the skeleton of particulate matter. Since oxygen is at a

sufficiently high temperature, a majority of the soot particles may undergo oxidation during all stages

of formation.

3.3.1 Measurement of Smoke

Two popular methods of measuring smoke are using filter smoke meter and opacity meter. The two

meters are highly developed in the last few years with superior control over temperature, flow and

automatic cleaning of optical parts enabling repeatability of data. They measure essentially

concentration of carbon soot as to be seen later and are highly correlated.

3.3.1.1 Filter paper method, Filter Smoke Number (FSN)

A sample of the exhaust gas is taken from the exhaust gas line with a probe through a pipe maintained

at constant temperature and drawn through a filter paper, Figure 6 [10, 11 and 12]. The blackness of

the filter paper caused by the exhaust soot is quantified by measuring the light reflected by the paper

when light from a controlled source is applied on it. The blackness mostly depends on the soot

5

concentration in the gas and the "effective sampling length". The Filter Smoke Number (FSN) 0 is

assigned to clean filter paper and the “completely” black paper is assigned the value FSN 10.

Figure 6 Filter smoke meter: (1) Effective sampling length, (2) Dead volume, (3) Take out volume and (4) Filter area

3.3.1.2 Opacity meter, Opacity %

Figure 7 Opacity meter probe

The exhaust gas is sampled into the probe by a diaphragm sampling pump into a measurement

chamber, Figure 7 [13]. The flow and the temperature (standard temperature 100°C; adjustable from

70 ... 120°C) are maintained constant to assure repeatability and high accuracy, irrespective of engine

operating conditions. In the chamber, the flow is divided into two flows in opposing directions in a

pipe. Light from a controlled source is sent through an orifice at intensity, I0 and it travels through the

pipe with an effective optical length of 430 mm. The sensitivity of the detector is optimised for low

noise to signal ratio and care is taken for signal stability. Let the intensity sensed by the detector be I.

Then the light extinction, the absorption coefficient, k, (m-1) and opacity, N (%) are related to the

measurement length, L (430 mm) as follows.

Eq. 1

Eq. 2

6

3.3.1.3 Photo-acoustic sensing

Photo-acoustic sensors are becoming more and more accurate to measure soot concentration and in

turn particle numbers for very low emission engines using diesel particulate filters [14] for transient

experiments.

3.3.1.4 Correlation of soot in PM, FSN and Opacity

Filter Smoke Number, measured with a Smoke Meter as the AVL 415S [12] represents the soot in the

particulates. On the other hand, the opacity, N (%) is the attenuation of light intensity influenced by

the soot. Hence, a correlation between FSN and opacity exists. Experiments on many engines in five

laboratories showed encouraging relationship as follows by AVL [12] for smoke number less than 4.0,

Figure 8.

Eq. 3

Figure 8 Relationship between FSN and opacity (measured in 5 different laboratories) [12]

Mörsch and Sorsche [15] similarly showed soot separated from total particulates by thermogravimetry

is strongly correlated with the opacity of the exhaust gases from 0 to 3%, Figure 9. Ref. [16] shows

soot concentration measured using optical LII method showed strong relationship with filter smoke

number in the range of 0 to 2. Therefore, the use of FSN for steady state measurement and opacity for

steady and transient measurement of soot concentration is reliably established in various laboratories

with a high correlation coefficient. Kirchen et al [17] corroborated AVL correlation of FSN and soot

concentration in the exhaust for two different fuels for FSN range 0 to 4, Figure 10. The sensitivity of

the AVL415S smoke meter is down to 0.002 FSN (20 µg/m3 soot concentration) and is proven

repeatable at steady modes up to Euro V emission standards where the PM limit is 0.02 g/kW h. The

FSN to soot concentration (mg/m3) correlation is given by Christian et al [10 and 18] and the AVL

product literature [19] as follows up to FSN of 8. FSN is determined by a linear measurement of filter

paper blackening using a gas column of 405 mm effective length. Eq. 5 is used recently in reference

[20] with a pre-exponent factor 4.95 instead of 5.32 and an exponent of 0.38 instead of 0.31.

Eq. 4

Eq. 5

0

5

10

15

20

25

30

35

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Op

acit

y, %

Filter Smoke Number, FSN

7

Figure 9 Calibration of opacimetry [15]

Figure 10 Soot emission measured using the AVL micro soot sensor and FSM for operation with two fuels; empirical correlations between soot concentration and FSN is shown [17]

4 Calculation of Total Particulate Matter Greeves et al [21] showed a strong additive relationship between soot concentration derived from

Filter Smoke Meter and hydrocarbons with the PM measured in the exhaust for engines using high

pressure Electronic Unit Injectors. Smoke and all the particulates are expressed in g/m3.

Eq. 6

Eq. 7

Eq. 8

The above calculated total particulate assumes that 27% of the gaseous HC goes to Soluble Organic

Fraction (SOF) in the particulate without any oxidation catalyst. Sulphates are considered negligible

y = 0.239xR² = 0.9907

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.000 0.005 0.010 0.015 0.020 0.025 0.030

Ther

mo

grav

imet

ry, g

/m3

Ln (100/(100-(T-T0))

0

20

40

60

80

100

120

140

160

180

200

0 1 2 3 4

Soo

t C

on

cen

trat

ion

, m

g/m

3

Filter smoke Number

Fuel 2

Reference fuel

Eq. 4 (AVL Correlation)

Eq.5

8

because very low sulphur fuel was used. An oxidation catalyst could be used to greatly reduce the SOF

part of the particulate with the requirement for a low sulphur fuel to minimise formation of sulphate.

4.1 PM Model Based on a number of tests, an additive PM model is developed. For each mode of the cycle, e. g., 8-

mode C1 cycle as per ISO 8178, the flows of soot, sulphate and hydrocarbons are calculated

Eq. 9

If smoke is measured in opacity %, convert to FSN using Eq. 3.

Soot concentration at NTP from Eq. 5

Eq. 10

Eq. 11

Eq. 12

Eq. 13

Where 0.092, 0.10 and 0.05 are the empirical contribution factors towards formation of sulphate, fuel

derived hydrocarbons and oil attached to particulate matter.

Summing up equations 10, 11, 12 and 13 of flows of soot, sulphate, hydrocarbon and lubricating oil in

PM, the flow rate of total particulate matter at a given mode is obtained. When the relevant weighting

factors for the test cycle (e. g., C1 cycl are used to obtain the weighted PM flow and then divided by

the weighted power, the specific PM in g/kWh for the chosen cycle is obtained.

4.2 Experimental validation Various engines of swept volumes from 1.3 litre to 3.6 litre were selected. These are naturally

aspirated, turbocharged and turbocharged after-cooled for different applications and power ratings.

These engines are essentially for off road applications namely tractors, cranes and earth movers as

well as power generation satisfying Indian and European emission standards of the past and the

present (see table below). The PM predicted by the additive model described above and the measured

using partial flow dilution tunnel is also tabulated and shown in Figure 11. The correlation coefficient,

R is 97% and the prediction on an average is 1.8% less than measured. Such a deviation is of the order

of PM measured using different tunnels, e. g., partial flow tunnel and full flow tunnel.

5 Discussions and Conclusions Insufficient resolution, instability and noise plagued smoke meters and opacity meters of yore. The

advent of advanced measurements under controlled conditions of temperature, pressure and flow

enabled highly accurate and repeatable measurement of smoke even at very low levels. Cross

sensitivity in opacity meters due to absorption of green light component by nitrogen dioxide could be

a problem when present in large amounts especially after highly doped diesel oxidation catalysts

where substantial nitric oxide is converted to nitrogen oxides. Such a problem does not exist in Filter

smoke instrument, whereas it is not amenable to transient measurement of smoke. But for these

9

minor irritants modern opacity meters and filter smoke meters are highly cost effective and can be

used during development of engine combustion or tuning the fuel injection system without spending a

fortune on samplers using either expensive partial flow tunnel or more expensive full flow tunnels.

Insensitivity of opacity meter to very small particles could be a problem in case of ultra low emission

engines [22]. Again filter smoke meter could help here if the sampled volume is increased inversely

proportionately.

The paper showed that particulate matter from diesel can be estimated with high confidence and

within errors of the order found in the partial flow/full flow tunnels for PM in the range of 0.02 to 0.4

g/kW h, which is in the scope of the emission standards accepted by most of the countries in the

world.

Figure 11 Predicted PM and Measured PM

6 References [1] Kittelson, D.B. (1998), Engines and Nanoparticles: A review. Journal of Aerosol Science 29: 575–

588.

[2] Ensor, D.S. and Pilat, M. J., Calculation of Smoke plume opacity from Particulate Air Pollutant

Properties Air Pollution Control Association Journal, vol. 21, no. 8, August, 1971

[3] Majewski, W. A. and Jääskeläinen, H., Smoke Opacity: DieselNet Technology Guide » Measurement

of Emissions, www.DieselNet.com. Ecopoint Inc. Revision 2013.07

[4] Twigg, M. V., Cleaning the Air We Breathe – Controlling Diesel Particulate Emissions from

Passenger Cars, Platinum Metals Rev.,2009,53,(1),27, doi:10.1595/147106709x390977

y = 0.9821xR² = 0.9383

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.00 0.10 0.20 0.30 0.40

Pre

dic

ted

PM

, g/k

Wh

Measured PM as per ESC, ISO 8178 C1 or D2 cycle, g/kWh

NA

T

TAA

10

[5] Vojtíšek, M., Pechout, M., Mazač1, M., Dittrich1, A., Čihák1, M., Particulate Matter Measurement

with an improvised Full-Flow Dilution Tunnel, Slovakia University of Zilina Department of

Automotive Technology, XLII International Scientific Conference of Czech And Slovak University

Departments and Institutions Dealing with the Research of Combustion Engines September 8 - 9,

2011

[6] Particle measurement program, Heavy Duty Inter-Laboratory Correlation Exercise: Final Report

Submitted by the expert from the United Kingdom, Economic Commission for Europe, Inland

Transport Committee, World Forum for Harmonization of Vehicle Regulations. Working Party on

Pollution and Energy, Sixtieth session, Geneva, 8-11 June 2010

[7] Gaseous Emissions, DieselNet Technology Guide, What Are Diesel Emissions, www.DieselNet.com.

Ecopoint Inc. Revision 2010.03d,

[8] Akamatsu, T. , Hack, R., McDonell, V., and Samuelsen, S., Evaluation of the Effects of Carbon to

Hydrogen Ratio and Sulphur Level in Fuel on Particulate Matters From Micro Gas Turbine Engine,

Journal of Engineering for Gas Turbines and Power, Volume 136, Issue 2

[9] Tornehed, P., Particulate Emissions Associated with Diesel Engine Oil Consumption, , Doctoral

thesis, Department of Machine Design, Royal Institute of Technology, SE-100 44 Stockholm, TRITA –

MMK 2010:09, ISSN 1400-1179, ISRN/KTH/MMK/R-10/09-SE, ISBN 978-91-7415-759-8

[10] Christian, R., Knopf, F., Jaschek, A., and Schindler, W., 1993. “Eine neue Messmethodik der

Bosch-Zahl mit erhoehter Empfindlichkeit”, Motortechnische Zeitschrift, 54, pp. 16–22.

[11] AVL List GmbH, 2005. Smoke Value Measurement with the Filter-Paper-Method: Application

Notes. Tech. rep.

[12] Smoke Value Measurement with the Filter Paper Method, Application Notes, AVL1007E, Rev.

02, June 2005

[13] AVL 439 Opacimeter Rev. 03 / SN 1569, SW ver. Ox1.25 and later (AVL 4210), November 2003

[14] Schindler, W., Haisch, C., Beck, H. A., Niessner, R., Jacob, E., Rothe, D., A Photo acoustic Sensor

System for Time Resolved Quantification of Diesel Soot Emissions, SAE 2004-01-0968, 2004

[15] Mörsch, O., and Sorsche, P., Investigation of Alternative Methods to Determine Particulate

Mass, DaimlerChrysler Alternative Particulate Emissions Measurement, DaimlerChrysler AG

[16] Mercer, C., Optical Metrology for Fluids, Combustion and Solids, Springer, 2003, pp 247

[17] Kirchen, P. , Boulouchos, K., Obrecht, P., and Bertola, A. Exhaust-Stream And In-Cylinder

Measurements And Analysis of the Soot Emissions from a Common Rail Diesel Engine using Two

Fuels, Proceedings of the ASME 2009 Internal Combustion Engine Division Fall Technical

Conference, ICEF2009, September 20-24, 2009, Lucerne, Switzerland

[18] Sluder, C. S., and Wagner, R. M., An Estimate of Diesel High-Efficiency Clean Combustion

Impacts on FTP-75 Aftertreatment Requirements, SAE 2006-01-3311, 2006

[19] AVL Model 415S Smoke Meter Product Literature, Updated October 15, 2002

[20] Kim, Y. J., Integrated Modelling And Hardware-In-The-Loop Study For Systematic Evaluation Of

Hydraulic Hybrid Propulsion Options, Ph.D. Thesis, Mechanical Engineering, University of Michigan,

2008

[21] Greeves, G., Tullis, S., and Barker, B., Advanced Two-Actuator EUI and Emission Reduction for

Heavy-Duty Diesel Engines, SAE2003-01-0698, 2003

[22] Roessler, D.M. and Faxvog, F. R., Opacity of black smoke: calculated variation with particle size

and refractive index, Applied Optics, Vol. 18, Issue 9, pp. 1399-1403, 1979

11

Table 1 Engines, aspiration, Power, smoke and PM

Swep

t vo

lum

e

Cyl

ind

ers

Asp

irat

ion

Emis

sio

n s

tan

dar

d

Po

we

r, M

etri

c h

p

Rat

ed s

pee

d, r

pm

smo

ke

Lub

rica

tin

g O

il C

on

sum

pti

on

, g/h

Pre

dic

ted

PM

, g/

kW h

Mea

sure

d P

M, g

/kW

h

1.3 2 NA TIIIA 24.0 3000 0.040 5 0.111 0.117

1.3 2 NA CPCB II 10.8 1500 0.340 6 0.134 0.127

1.7 2 NA CPCB I 21.8 1800 0.442 4.5 0.137 0.123

2.5 3 NA CPCB I 23.7 1500 0.580 5 0.162 0.174

2.5 3 NA TIIIA 36.0 2000 0.469 5 0.307 0.300

2.7 3 NA EU Stage IIIA 49.3 2250 0.362 5 0.317 0.285

2.7 3 NA TIIIA 46.0 2250 0.379 5 0.149 0.108

3.3 4 NA CEVBSIII 35.8 2000 0.240 5 0.21 0.223

3.3 4 NA CPCB I 32.1 1500 0.300 5 0.118 0.120

3.3 4 NA CPCB1 33.4 1800 0.090 5 0.070 0.072

3.6 4 NA EU Stage IIIA 66.3 2200 0.253 5 0.207 0.228

3.3 4 T CPCB I 39.1 1500 0.210 5 0.172 0.181

3.6 4 T CEVBSIII 60.0 2200 0.120 5 0.130 0.152

3.6 4 T CEVBSIII 59.0 2300 0.128 5 0.160 0.153

2.6 3 TAA TIIIA 85.0 2200 1.218 5 0.346 0.339

2.7 3 TAA TIIIA 58.0 2200 0.210 5 0.197 0.204

2.7 3 TAA CPCBII 30.2 1500 0.308 5 0.220 0.213

2.7 3 TAA CPCBII 38.5 1500 0.513 5 0.162 0.148

3.6 4 TAA TIIIA 75.0 2200 0.113 5 0.090 0.123

3.6 4 TAA TIIA 82.0 2200 0.098 5 0.112 0.111

3.6 4 TAA CPCBII 40.5 1500 0.484 5 0.180 0.160

a. Notes:

b. TIIA- India emission standard for Tractor equivalent to EU Stage IIA c. TIIIA- India emission standard for Tractor equivalent to EU Stage IIIA d. CEVBSIII-India emission standard for Construction Equipment equivalent to EU Stage IIIA e. CPCBI, CPCBII- India Genset emission standards from year 2005 and 2014 f. TAA-Turbocharged aftercooled g. NA-Naturally aspirated h. T-Turbocharged