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Frontiers in Energy

ISSN 2095-1701

ISSN 2095-1698(Online)

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Front. Energy    2022, Vol. 16 Issue (2) : 277-291    https://doi.org/10.1007/s11708-021-0731-6
RESEARCH ARTICLE
An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency
Lei ZHU(), Zhan GAO, Xiaogang CHENG, Fei REN, Zhen HUANG
Key Laboratory for Power Machinery and Engineering of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200030, China
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Abstract

An integrated and systematic database of sooting tendency with more than 190 kinds of fuels was obtained through a series of experimental investigations. The laser-induced incandescence (LII) method was used to acquire the 2D distribution of soot volume fraction, and an apparatus-independent yield sooting index (YSI) was experimentally obtained. Based on the database, a novel predicting model of YSI values for surrogate fuels was proposed with the application of a machine learning method, named the Bayesian multiple kernel learning (BMKL) model. A high correlation coefficient (0.986) between measured YSIs and predicted values with the BMKL model was obtained, indicating that the BMKL model had a reliable and accurate predictive capacity for YSI values of surrogate fuels. The BMKL model provides an accurate and low-cost approach to assess surrogate performances of diesel, jet fuel, and biodiesel in terms of sooting tendency. Particularly, this model is one of the first attempts to predict the sooting tendencies of surrogate fuels that concurrently contain hydrocarbon and oxygenated components and shows a satisfying matching level. During surrogate formulation, the BMKL model can be used to shrink the surrogate candidate list in terms of sooting tendency and ensure the optimal surrogate has a satisfying matching level of soot behaviors. Due to the high accuracy and resolution of YSI prediction, the BMKL model is also capable of providing distinguishing information of sooting tendency for surrogate design.
Keywords sooting tendency      yield sooting index      Bayesian multiple kernel learning      surrogate assessment      surrogate formulation     
Corresponding Author(s): Lei ZHU   
Online First Date: 13 April 2021    Issue Date: 25 May 2022
 Cite this article:   
Lei ZHU,Zhan GAO,Xiaogang CHENG, et al. An assessment of surrogate fuel using Bayesian multiple kernel learning model in sight of sooting tendency[J]. Front. Energy, 2022, 16(2): 277-291.
 URL:  
https://academic.hep.com.cn/fie/EN/10.1007/s11708-021-0731-6
https://academic.hep.com.cn/fie/EN/Y2022/V16/I2/277
Serial name S4-1 S4-2 S4-3 S4-4 S4-5 S4-6 S4-7 S4-8 S4-9 S4-10 S4-11
n-hexadecane/% 0 10 20 30 40 50 20 20 20 20 20
iso-cetane/% 50 40 30 20 10 0 30 30 30 30 30
Decalin/% 20 20 20 20 20 20 0 10 30 40 50
1-methylnaphthalene/% 30 30 30 30 30 30 50 40 20 10 0
n-hexadecane/% 0 12.3 24.7 37.0 49.4 61.7 24.7 24.7 24.7 24.7 24.7
iso-cetane/% 61.7 49.4 37.0 24.7 12.3 0 37.0 37.0 37.0 37.0 37.0
Decalin/% 15.1 15.1 15.1 15.1 15.1 15.1 0 7.5 22.6 30.3 37.9
1-methylnaphthalene/% 23.2 23.2 23.2 23.2 23.2 23.2 38.6 30.9 15.5 7.8 0
Tab.1  Blending factions of test surrogate fuels from Category 2
Fig.1  Schematic of experimental setup.
Fig.2  Histogram of all YSI in data set.
Fig.3  Flowchart of Bayesian multiple kernel learning method.
Fig.4  YSIs measured in the present study versus that in Ref. [6]. (The red dot line is the linear fit line.)
Fig.5  Predicting performance of proposed model with BMKL method in testing procedure.
Method Deviationa) Mean relative errorb)/% RMSEc) Correlation coefficient
Mean Maximum
BMKL 2.57 10.53 10.37 4.23 0.986
LA 3.98 17.48 15.20 5.12 0.966
Tab.2  Statistical analysis of testing results for BMKL and LA methods
Components Mole fraction/% YSI measured
3-component surrogate fuel n-hexadecane 41.3 45.4
HMNa) 36.8
1-methylnaphthalene 21.9
5-component surrogate fuel n-hexadecane 21.6 46.8
HMN 26.0
1-methylnaphthalene 20.7
decalin 16.2
n-octadecane 15.5
7-component surrogate fuel n-hexadecane 21.5 38.0
HMN 25.8
1-methylnaphthalene 13.7
decalin 8.1
n-octadecane 15.4
n-butylbenzene 8.1
n-butylcyclohexane 7.4
Tab.3  Compositions of diesel surrogate fuels in Ref. [35] and YSI measured in present study
Fig.6  Comparison of measured and predicted sooting tendencies of a real diesel and its surrogate fuels.
Fig.7  Comparison of YSI values obtained by experiment and different numerical approaches for 2nd generation surrogate of POSF 4658 proposed in Ref. [11].
Components Mole fraction/% Measured YSI
Soybean biodiesel Methyl palmitate 13.6 19.7
Methyl linoleate 41.5
Methyl oleate 30.2
Methyl stearate 6.0
Waste cooking oil biodiesel Methyl palmitate 33.4 17.8
Methyl linoleate 17.9
Methyl oleate 35.0
Methyl stearate 6.5
SB surrogate a) Methyl decanoate 62.1 17.2
n-hexadecane 16.7
methyl trans-3-hexenoate 7.9
1, 4-hexadiene 13.3
WB surrogate b) Methyl decanoate 60.5 14.9
n-hexadecane 24.9
Methyl trans-3-hexenoate 9.0
1, 4-hexadiene 5.6
Tab.4  Main contents of two real biodiesel fuels and their surrogates, and their measured YSI in this study
Fig.8  Comparison of YSI values for real biodiesel fuels and their surrogates (BS 2-1: 50% n-decane/50% methyl octanoate by mole; B S2-2: 50% n-heptane/50% methyl decanoate by mole).
n-dodecane iso-octane 1,3,5-trimethylbenzene n-propylbenzene H/C MW/(g•mol–1) DCNb) TSIc) YSId)
Target fuel: Jet-A POSF 4658a) 1.96a) 142±20a) 47.1a) 21.4a) 40.1e)
1 40.4% 29.5% 7.3% 22.8% 1.96 138.7 47.1 21.4 35.5
2 39.3% 31.6% 13.8% 15.3% 1.97 138.0 47.1 21.5 36.3
3 40.1% 31.3% 27.7% 0.7% 1.97 138.4 47.0 22.6 38.5
4 35.4% 33.2% 0 31.4% 1.95 135.4 47.0 21.4 35.0
5 36.1% 33.3% 1.6% 28.9% 1.95 136.3 47.1 21.1 34.9
6 39.6% 30.3% 14.5% 15.6% 1.96 138.3 47.1 22.1 36.7
7 38.9% 31.3% 12.0% 17.7% 1.96 137.9 47.1 21.7 36.2
Tab.5  Mixture mole fractions and predicted combustion properties of surrogate candidates for Jet-A POSF 4658
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