Subscriber access provided by UNIVERSITY OF ADELAIDE LIBRARIES
Article
Designing a Surrogate Fuel for Gas-to-Liquid (GTL) Derived Diesel Hanif Ahmed Choudhury, Saad Intikhab, Sawitree Kalakul, Muzammiluddin Khan, Reza Tafreshi, Rafiqul Gani, and Nimir O Elbashir Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b00274 • Publication Date (Web): 14 Aug 2017 Downloaded from http://pubs.acs.org on August 19, 2017
Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.
Energy & Fuels is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.
Modeling
Combustion
GTL Diesel
Physicochemical property
Density Viscosity Flash Point Distillation ……………..
Diesel
GTL Diesel
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
Energy & Fuels
Surrogate
Page 1 of 34
Emission profile ACS Paragon Plus Environment
Energy & Fuels
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
1 2 3
Designing a Surrogate Fuel for Gas-to-Liquid (GTL) Derived Diesel
4 5 6 7 8 9 10 11 12
H.A. Choudhurya, S. Intikhaba, S. Kalakulb, M. Khanc, R. TafreshiC, R. Ganib and N.O. Elbashira,d
13
a
b
Chemical Engineering Program, Texas A&M University at Qatar, 23874 Doha, Qatar Department of Chemical and Biochemical Engineering Program, Technical University of Denmark, DK-2800 Lyngby, Denmark a Mechanical Engineering Program, Texas A&M University at Qatar, 23874 Doha, Qatar d
Petroleum Engineering Program, Texas A&M University at Qatar, 23874 Doha, Qatar
Keywords: GTL diesel, surrogate, fuel properties, combustion, emission
14
ACS Paragon Plus Environment
Page 2 of 34
Page 3 of 34
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Energy & Fuels
Abstract: Synthetic diesel fuel produced from natural gas via the Gas to Liquid (GTL) technology is referred to as ultra-clean fuel but still challenged for full certification as diesel fuel. GTL diesel lacks certain hydrocarbons and chemical constituents, which although are benign to environment, result in a trade-off in performance when used in a diesel engine. To boost GTL diesel physicochemical properties and thereby enable its use in conventional diesel engines, GTL diesel needs improvement. This can be achieved by mixing suitable additives to the GTL diesel and through the development of surrogate fuels that have fewer components. Screening of thousands of additives is a tedious task and can be done efficiently via computer based modeling to quickly and reliably identify a small number of promising candidates. These models are used to guide the formulation of five surrogates and predict their physicochemical properties. These surrogates are further verified using rigorous mathematical tools as well as through advanced experimental techniques. An optimal surrogate MI-5 is identified, which closely mimics a GTL diesel-conventional diesel blends in terms of its physicochemical properties. An engine study for the surrogate is also performed to understand the effect of physicochemical properties on combustion as well as emission behavior of the fuel. MI-5 exhibited an optimal torque at higher load conditions. A reduction of 11.26% NOx emission for MI-5 is observed when compared to conventional fuel. At higher loads, diesel fuel surpasses the total hydrocarbon (THC) emissions for both the surrogate and the GTL fuel. No significant variation in CO and CO2 emissions for MI-5, GTL diesel and conventional diesel are observed. Analysis of combustion as well as emission behavior of the fuels helps to understand the role of physicochemical properties on the performance of the fuel.
2 ACS Paragon Plus Environment
Energy & Fuels
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 4 of 34
1
1. Introduction
2
Gas-to-Liquid (GTL) fuels derived from the Fischer-Tropsch synthetic process have gained increased
3
attention due to their significant supply coming from major plants such as the Pearl GTL plant in
4
Qatar1. The focus on GTL fuels is mainly driven by the need to reduce emissions of greenhouse gas
5
(GHG) and their ability to complement dwindling world fuel supplies
6
environmentally benign (due to the lack of aromatics and sulfur), GTL fuels fall short of meeting
7
critical physical properties that affect fuel combustion behavior, as opposed to conventional crude oil
8
based fuels. For example, pure GTL diesel when tested in an unmodified engine control unit of a test
9
engine, exhibited slightly lower torque, power and brake thermal efficiency (BTE) due to lower density
10
and bulk modulus of the GTL diesel4. Therefore, GTL fuels are typically blended with crude oil-based
11
conventional fuels to be used in conventional compression ignition (CI) engines5. At present there are
12
no established specifications for blending GTL diesel with conventional diesel as in the case of
13
synthetic paraffinic kerosene (SPK) blends (ASTM 7566/1655), wherein an upper limit of 50 (vol%) of
14
SPK is permitted in conventional jet fuel. Therefore, it is contingent on refineries to dictate the blend
15
ratios. Typically, GTL fuels mostly comprise n- and iso-paraffinic compounds. However, when GTL
16
diesel is blended with conventional diesel, it yields a complex blend that consists of aromatic and cyclo-
17
compounds in addition to n- and iso- alkanes. Formulating such complex blends consisting of hundreds
18
of individual compounds via computer aided modeling naturally poses a tremendous optimization
19
challenge. Such techniques require details of each compound, chemical kinetic rate constants and
20
thermodynamic property model parameters, all of which are currently not available 6. Moreover, testing
21
of these blends will require excessive amount of time and resources.
22
A feasible alternative is to employ a ‘surrogate’ fuel that represents a GTL diesel-blended conventional
23
diesel. A surrogate is a mixture of a smaller number of chemical compounds with known compositions
24
and properties. Dooley et al.7,8 reported development of a four component surrogate for jet fuel to
25
emulate the gas phase combustion kinetics of their fuel. They provided a comprehensive data set of
26
combustion kinetics to aid numerical modeling of combustion process of similar complex fuels. They
27
proposed the conceptual theory of fuel oxidation and used it to provide a methodology for the
28
formulation of surrogates.
29
Shrestha et al.9 reported the development of six different surrogates using a MATLAB program to
30
emulate the ignition delay and rate of heat release of the JP-8 surrogate for diesel engine application.
31
They developed a MATLAB code to identify surrogate components that closely match the target fuel.
32
More recently, Kang et al.10 performed combustion as well as experimental validation studies on Jet-A
33
surrogates developed via a model-based optimizer. Their experimental validation study revealed that
34
surrogates developed using a model-based optimizer show acceptable agreement with the target Jet-A. 1 ACS Paragon Plus Environment
2-3
. Despite being
Page 5 of 34
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Energy & Fuels
11
1
Pitz and Mueller
2
and discussed the experimental and modeling work on some critical diesel surrogate compounds. Prak
3
et al. 6 studied the chemical composition and physical properties of algal-based hydro treated renewable
4
diesel (HRD) to develop a surrogate mixture. They tested the surrogate in a Yanmar engine along with
5
HRD fuel to compare and evaluate the engine performance in terms of start of ignition, ignition delay,
6
overall combustion duration and maximum rate of heat release 6. Mueller et al.
7
four ultra-low sulfur diesel surrogate mixtures to be investigated in CI engine and combustion vessel
8
experiments.
9
Although there have been numerous reports on surrogate development for conventional diesel fuel and
10
Jet fuel to understand their behavior during combustion, no similar attempt was made to develop a
11
surrogate for GTL blended diesel fuel. The goal of this study is to develop a surrogate that can represent
12
a typical blend of GTL diesel and conventional diesel. Such a blend can therefore be used as a baseline
13
for future studies aiming to improve the performance of GTL diesel either via blending of conventional
14
diesel or via mixing of additives to the pure GTL diesel. Therefore, an attempt is made in this paper to
15
formulate a representative GTL diesel-conventional diesel blend surrogate whose physio-chemical
16
properties are tailored through a non-empirical Equation of State based (EOS) model. The purpose of
17
this study is to gain insight into the effect of the surrogate’s physicochemical properties on its
18
combustion as well as emission behavior. For the sake of simplicity, the “GTL diesel-conventional
19
diesel blend surrogate” will be referred to as “surrogate” throughout this paper. The data presented
20
herein will help enable combustion modeling of real fuel and characterization of surrogate's combustion
21
behavior in the engine will give the insight to understand their capabilities/limitations to represent the
22
targeted fuel. The paper is organized as follows: Section 2 discusses surrogate fuel design and
23
experimental testing methodologies; Section 3 describes an approach to experimentally verify the
24
model predictability of the fuels' physical properties as well as the role of the chemical compounds
25
present in the surrogate on the fuel’s physicochemical properties. Section 3, also includes engine
26
performance tests based on combustion and emission behavior for the surrogate diesel, GTL diesel, and
27
conventional fuel. The last part of this paper is the conclusion and recommendation for future work.
have reviewed the progress in the area of surrogates for conventional diesel fuels
12
formulated a set of
28 29
2. Materials and methods
30
Yunus et al.2 reported a systematic methodology to design tailor-made blended products using a
31
computer-aided technique. They developed three supporting tools; a chemical database, a property
32
model library and a blend composition optimizer associated with their methodology. Further, they
2 ACS Paragon Plus Environment
Energy & Fuels
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
1
applied their methodology to case studies involving the design of gasoline and lubricant blends as a
2
validation of their technique.
3 4
A similar methodology has been adopted-applied in this work to formulate five surrogates that represent
5
a GTL diesel-conventional diesel blend to understand the role of chemical compounds in the blend and
6
then to fine-tune the physicochemical properties that affect the combustion behavior of the surrogate
7
fuel. Various compositions are proposed via this adopted technique and changes in the final
8
compositions of the surrogates are fine-tuned in conjunction with the experimental feedback. The
9
experimental campaign consists of preparing different surrogates and measuring important properties
10
that have been predicted by the model-based technique, such as, density (ρ), kinematic viscosity (ν),
11
vapor pressure (VP), flash point (Tf) and high heating value (HHV). Other properties that were not
12
constrained in modeling, such as, cloud point (CP), pour point (PP), distillation profile, and calculated
13
Cetane index (CCI) that are critical for a diesel fuel have also been measured and compared with
14
conventional diesel and pure GTL diesel. The last step of the investigation involved a preliminary
15
engine test with the surrogate to investigate its capability to be run on an un-modified set-up and to
16
ascertain compatibility with the conventional diesel and GTL diesel. A more comprehensive
17
combustion study will be undertaken to investigate the effect of physicochemical properties on
18
combustion and emission behavior of the fuel in detail. Following topics are included in this section: (i)
19
brief description of the algorithm and the models used to design the blends representing the surrogates,
20
(ii) list of chemical compounds, blending techniques and blend verification methods, (iii) experimental
21
techniques to measure the physicochemical properties of the blends, and (iv) methods used for the
22
combustion and emission study.
23 24
2.1.
Diesel target properties and calculation models
25
The surrogates investigated in this study were formulated in-silico via the workflow shown in Figure 1.
26
The objective of step 1, termed ‘problem definition’, is to identify the product needs and translate them
27
into physicochemical properties with target values that need to be matched. This step requires prior
28
knowledge coming from a thorough understanding of the material physics, thermodynamics, reaction
29
kinetics and chemistry related to the use of the product (in this case, diesel), as well as knowledge from
30
peer-reviewed articles and other information databases. As Yunus et al.2 pointed out, there is clearly a
31
need to develop a reliable knowledge base with information from relevant publications, patents etc.,
32
which has been developed for design of tailor-made diesel. The new formulated surrogate should have
33
good fuel performance and meet or exceed stringent requirements for worldwide fuel handling and 3 ACS Paragon Plus Environment
Page 6 of 34
Page 7 of 34
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Energy & Fuels
1
product standards as listed in Table 1. The objective of step 2 is to formulate the tailor-made diesel
2
blend design as a mathematical optimization problem, the Mixed Integer Non-Linear Programming
3
(MINLP) problem, using molecular, structural, mixture, target-property and process constraints. , as
4
presented as Eq. (1) – (3). The linear target property constraints are: HHV, ρ and -logLC50 (Eq. 2), while
5
the non-linear constraints are: RVP, flash point temperature (Tf) and kinematic viscosity (Eq. 3). The
6
property model equation details are given in Appendix.
7 ( )
(1)
≤ ≤
(2)
(, ) ≤
(3)
8
where is molar fraction of compound i in the mixture; is the pure component property j of
9
compound i; is non-linear property K; X is a vector of compositions; D is a vector of model
10
parameters; UB is a upper bound value of the linear property; LB is a lower bound value of the linear
11
property; Details of the model may be found in Yunus et al2 and Gani et al13. In step 3, a solution to the
12
above MINLP problem is achieved14 via strategies such as: database search; generate-test approach
13
(first generate candidates that satisfy the constraints and then order the feasible candidates in terms of
14
their objective function values); simultaneous solution approach (use an appropriate optimization
15
algorithm to solve the MINLP problem); and decomposition approach (decompose the main problem
16
into sub-problems and solve these sub-problems according to a predefined solution order). The
17
computer-aided tools used in this step are highlighted in Figure 1. Finally, in step 4, a small number of
18
promising candidates from step 3 are selected for experimental verification and fine-tuning either
19
through rigorous computational model-based tests or experimental tests as suggested by the Design of
20
Experiment toolbox. In this step, the stability of the product, the desired performance, the target
21
properties, the color, etc., are verified. Note that with the above problem formulation, while the final
22
selected surrogate will have a single final value, use of bounds on the target properties provides a more
23
flexible solution approach. This is because for a single target value, a fixed target value can be matched.
24
However, for more than one target property, it may not be possible to match all the fixed values of the
25
target properties. Thus, bounds help to find candidates from which the most appropriate can be selected.
26 4 ACS Paragon Plus Environment
Energy & Fuels
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Figure 1.
1 2 3
Table 1.
4
In this study five surrogates (namely MI-1 to MI-5) were formulated as described above. Their
5
compositions are given in Tables.
Table 2
6 7
2.2.
Chemical compounds’ procurement, handling and blending
8
All the chemicals used to prepare the surrogate fuels are listed in Table 3. A GTL diesel fuel and the
9
conventional diesel fuel are obtained from local venders.
10
Table 3
11 12
Approximately 500 ml. of each surrogate fuels are prepared by adding a desired volume of the chemical
13
compound to a container using different cleaned pipettes for each compound. The ASTM D5854
14
method is followed for preparing these blends. The prepared surrogate fuels are shaken vigorously for
15
about one minute to ensure proper mixing.
16
2.3.
17
For the all the blends along with GTL diesel and conventional diesel, the following physical properties
18
are determined using different equipment and standards.
19
•
20 21
•
•
•
30
Flash point, Tf is measured using Pensky-Martens Closed Cup Flash Point Tester (PM 93) by Stanhope-Seta according to ASTM D93.
•
28 29
Cold flow properties analyzer by Phase Technologies is used to measure cloud point, CP and Pour point, PP according to ASTM D5773 and ASTM D5972, respectively.
26 27
Kinematic viscosity, ν is measured using Stabinger viscometer by Anton Paar according to ASTM 7042.
24 25
Density, ρ is measured using a density meter (DMA 4100) by manufacturer Anton Paar according to ASTM D4052.
22 23
Properties testing methods
Vapor pressure, VP for the blends is measured using Equipment MiniVapXpert by manufacturer Grabner instrument according to ASTM D6378.
•
High heating value, HHV is measured using the bomb calorimeter by Parr Instrument according to ASTM D240. 5 ACS Paragon Plus Environment
Page 8 of 34
Page 9 of 34
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
1
Energy & Fuels
Distillation Curve is determined according to ASTM D86 using a Distillation Unit by Petrotest
•
2 3
Company. Cetane number, CN is calculated using ASTM D4737, which uses a correlation that has been
•
4
established between the ASTM CN and the density and 10%, 50% and 90% distillation recovery
5
temperatures of the fuel. This test method (D4737) is a supplementary tool for estimating Cetane
6
number when it is not possible to obtain result from ASTM D613. In this article, the CN is thus
7
described as Calculated Cetane Index (CCI). Please note that this test method is not a routine
8
method for measuring ASTM Cetane number; for verification, experimental measurement has to be
9
performed using ASTM D613.
10
2.4.
Combustion and emission study
11
The combustion study is performed in an air-cooled Yanmar Direct Injection LV100 series single
12
cylinder diesel engine. The key purpose of the experiment is to analyze and perform a detailed engine
13
performance and emission characteristic study using the three different fuels: conventional diesel, GTL
14
diesel and a surrogate. No modifications are made to the engine in any way for testing the different
15
fuels. The specifications of the engine are given in Table 4.
16
Table 4
17
A hydraulic brake dynamometer is also utilized for the purposes of this experiment. The dynamometer
18
is operated in remote mode for all runs after the input of similar drive cycles for all fuels. The load
19
acting on the engine is increased or decreased via a hydraulic regulator. The RPM is maintained
20
constant via an in-built controller, which augments the throttle that in turn controls the input of fuel. A
21
load cell attached to the output shaft of the engine measures the resulting torque produced by the
22
engine. The sampling rates for both engine RPM and net resultant torque output are both set to 1 Hz.
23
Two specific runs at different RPMs are performed namely, 1500 and 2500 RPM, respectively. The
24
total run-time is 7 mins with load ranges varying from 5% to 100% being applied under identical step-
25
wise incremental conditions for all preceding engine RPMs. The central computer console then records
26
the different parameter values, such as, net torque (Nm), engine speed (RPM) and exhaust temperature
27
Tex (°C), via thermocouples placed in the exhaust manifold.
28
The engine is run for a statutory period of 5 min after the change of the fuel to ensure all fuel lines and
29
internal components of the engine have been saturated with the new fuel and to rule out any
30
discrepancies in the data obtained. The exhaust from the engine is sent to a pre-filtering element to help
31
remove the particulate matter (PM) and generated soot. The gases are then sent to the Horiba MEXA
32
7170 DEGR analytical emissions testing bench via heated sampling lines. The sampling rate for the
33
emissions study is also set to 1 Hz. The total hydrocarbon (THC) concentrations are measured by the 6 ACS Paragon Plus Environment
Energy & Fuels
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
1
principle of flame ionization detector (FID); CO and CO2 concentrations are measured via a non-
2
dispersive infra-red (NDIR) analyzers. NOx and its derivatives are measured via chemiluminescent
3
analyzers.
4
3. Results and discussion
5
The results are presented and discussed in the following order: the first part covers the model
6
predictability of the critical properties for five surrogate blends using advanced experimental setup as
7
well as the distillation profiles of our fuels in sections 3.1, 3.2 and 3.3; the second part discusses the
8
correlation between the physicochemical properties of the surrogates and their hydrocarbon
9
composition (section 3.4); and finally investigating the role of the physical properties on engine
10
performance via combustion and emission studies (sections 3.5 and 3.6).
11 12
3.1.
Composition of surrogate mixtures
13
Compositions of five surrogates are given in the Table 2. The first developed surrogate (MI-1) was
14
designed to represent, as closely as possible, typical GTL diesel, to act as a reference fuel. The second
15
surrogate (MI-2) is formulated to ensure it comprises iso-, cyclo- and aromatic compounds to closely
16
represent GTL diesel blended with conventional diesel. However, 2, 2, 4-trimethylpentane,
17
commercially known as iso-octane, present in the MI-2, has very low Tf ( −12 οC), which affects the
18
overall blend’s Tf . It is essential to have a high Tf value for the diesel fuel (>38 οC) since it determines
19
the safe handling of the fuel. As a result, the third surrogate (MI-3) was designed replacing iso-octane
20
with iso-cetane (Tf : 96 οC). It is to be noted that aromatic concentration in surrogates MI-2 and MI-3
21
was optimally predicted by the model to be 20 vol%, which is significantly higher than that of a typical
22
GTL-blended diesel fuel. Aromatics have an inverse effect on the emission profile of any fuel as their
23
presence in high concentrations will lead to incomplete combustion15. Therefore, a lesser concentration
24
is always desired to control the emission of fuel during its combustion. Additionally, Tetralin, used to
25
serve as the aromatic part in surrogates MI-2 and MI-3, is a cause for concern as it may produce
26
explosive peroxides on combustion as evident from the Safety Data Sheet provided by the supplier. It is
27
noted, there are a few reports in the open literature where combustion studies were performed with
28
Tetralin11,12,16–25, . In this study, a more conservative approach is followed and decision was made to (i)
29
lessen aromatic concentrations and, (ii) use a safer molecule than Tetralin.
30
Therefore, Tetralin was substituted with Toluene in MI-4. Since the deviation of predicted versus
31
experimental values of MI-4’s Tf and VP of MI-4 was found to be high, and additional additive was
32
recommended. As a result, in MI-5, n-Decane was added, which significantly improved RSD values 7 ACS Paragon Plus Environment
Page 10 of 34
Page 11 of 34
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Energy & Fuels
1
(Table 5). MI-5 comprises all the hydrocarbon building blocks similar to that of a GTL diesel-
2
conventional diesel blended fuel and can be regarded as an ‘optimal’ candidates for a surrogate.
3
Table 5
4 5 6 7
3.2.
Model prediction versus experimental measurements
8
To verify the model predictions on the physicochemical properties, the five surrogates are tested in the
9
Fuel Characterization Lab utilizing advanced analytical instruments. The experimental results of the
10
targeted properties (ρ, ν, VP, Tf and HHV) are measured and compared to the model predicted values as
11
given in Table 5. The variations between the model prediction and experimental results are expressed in
12
terms of the Relative Standard Deviation (RSD). RSD is defined as the ratio of standard deviation (σ)
13
and mean (µ) and is expressed as a percentage as shown in Eq. (4).
14
= /! × 100 (4)
15
Model predictions for MI-1 are found to be in close agreement with experimental results for all the
16
target properties viz. ρ, ν, VP, Tf and HHV as evident from their RSD values of 0.06%, 0.19%, 6.59%,
17
0.32% and 1.15%, respectively. Whereas, for MI-2, MI-3 and MI-5, small RSD values are observed for
18
only the following target properties: ρ, Tf and HHV as given in Table 5. It is worth noting that both ρ
19
and HHV values predicted by the model are in good agreement with experimental results for all
20
surrogates, which implies that the linear mixing rule assumption for calculating ρ and HHV for these
21
blends is appropriate. Elmalik et al 1 has also observed a linear trend with respect to constituent’s mass
22
fraction for ρ and HHV.
23
However, in the case of MI-4 the ν, VP and Tf are found to be in poor agreement with the experimental
24
results as evident from their large RSD values of 11.36%, 67.39% and 28.34%, respectively. MI-4
25
comprises approximately 45(vol%) of n-paraffinic compound in contrast to all other blends which
26
comprises ≥55(vol%). The greater volume of the other hydrocarbon classes (iso- and cyclo-) present in
27
MI-4 produces greater intermolecular interactions. This in turn renders a non-linearity in the target
28
properties of MI-4 that leads to this observed deviation in model predictions from experimental data.
29
The non-linearity is accounted for in revised calculations for MI-4 with models available in Virtual
30
Product-Process Design Lab (VPPD-lab)26. The new results of ν, VP and Tf are given in Table 6,
31
clearly shows an improvement in the RSD values.
32 8 ACS Paragon Plus Environment
Energy & Fuels
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 12 of 34
1
Table 6
2 3 4 5
3.3.
6
The distillation profile is not used here as a fitting criterion but rather to validate the formulated
7
surrogates against conventional diesel and GTL diesel fuels. Figure 2 shows the comparison of
8
distillation profiles of surrogates with pure GTL diesel and conventional diesel. Front end of the profile
9
(10 vol%) helps determining: (1) easing hot starting, (2) easing cold starting, (3) avoiding vapor lock,
10
and (4) lowering evaporation and running-loss emissions. Midrange of the profile (50 vol%) helps
11
determining:(1) fast warm-up and smooth running, (2) superior short-trip fuel economy, (3) good
12
acceleration and power, and (4) defense against carburetor icing and hot-stalling. Tail-end of the profile
13
(90 vol%) helps determining: (1) superior fuel economy after engine gets warmed up, (2) no engine
14
deposits, (3) minimal fuel dilution of crankcase oil, and (4) minimal volatile organic compound (VOC)
15
exhaust emissions.
16
MI-1 and MI-3 have a slightly higher initial boiling point (IBP) than that of GTL diesel. The IBP of MI-
17
1 and MI-3 indicates that these two fuels will have easy hot and cold starting. All the surrogates except
18
MI-1 have poor match with the middle and tail end of the distillation profile of GTL diesel. This
19
shortcoming can be attributed to the shorter carbon chain length compounds present in all the surrogate
20
blends except MI-1 which also comprises of C18 and C20 compounds. Pitz and Mueller
21
review also suggested that compounds, which lie in the carbon range (C10-C22) are more
22
representative of diesel fuel. It has been noted by Won et al.27 that the use of component that have a
23
molecular weight near the average for the real fuel would be beneficial to better match the distillation
24
property of the target fuel. The average molecular chain lengths of all the surrogate blends are given in
25
Table 7, and calculated by the method described in Jeng28. In the present study, it is observed that MI-1,
26
which has an average chain length of 14.5, shows an excellent match with the distillation profile of
27
GTL diesel. Whereas, all the other surrogate fuels (MI-2, MI-3 and MI-5) have a much lower average
28
chain length of approximately around carbon 11, which could be the reason behind a poor match with
29
the distillation profile of either GTL diesel or conventional diesel. Therefore, use of longer chain
30
compounds (C15-C16) in the blends will enable a better matching of the distillation profile of any
31
surrogate fuel with the GTL fuel.
32
Distillation Profile
Table 7
33 9 ACS Paragon Plus Environment
11
in their
Page 13 of 34
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Energy & Fuels
1
MI-2, MI-3 and MI-5 are more volatile as compared to MI-1 and therefore, the distillation profiles are
2
also towards much lower temperatures that infers an improved atomization and dispersion of fuel spray.
3
It may also lead to a more combustible air-fuel mixture as lower distillation characteristics ensure ease
4
of evaporation. Moreover, reduced smoke and Particulate Matter (PM) emissions are also expected for
5
these more volatile surrogates. It is worth mentioning that the scope of this work is not to compare the
6
results with ASTM specifications, however the distillation profile of MI-2, MI-3 and MI-5 comply with
7
Grade 1 and MI-1 and GTL diesel complies with Grade 2 of ASTM D975 specifications.
8
Figure 2
9 10
3.4.
Correlation between physicochemical properties
11
In this section comparisons of physicochemical properties of tested fuels are done to investigate the
12
role of constituent chemical compounds on individual fuel physicochemical properties. Each target
13
physicochemical property is contrasted with the one that could possibly influence the other.
14
3.4.1. Density (ρ) and kinematic viscosity (ν)
15
Figure 3 shows a comparison of ρ and ν of the surrogates with pure GTL diesel and conventional diesel.
16
MI-1 consists entirely of n-paraffinic compounds and is comparable with pure GTL diesel which is
17
highly paraffinic in nature. The lower ρ values for MI-1 and pure GTL diesel can be attributed to the
18
lack aromatics 1. The effect of aromatics on ρ is observed by increasing its concentration from MI-5
