Towards Model-Based Identification of Biofuels for Compression Ignition Engines

Dahmen, Manuel; Hechinger, Manuel; Villeda, Juan Victoria; Marquardt, Wolfgang

doi:10.4271/2012-01-1593

Cited by 24 publications

(24 citation statements)

References 44 publications

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“…Mathematical techniques such as multiple linear regression (MLR) [49,58,60,85] and partial least squares (PLS) [50] have been applied to analyze the compositional data and then deployed as tools to develop mathematical models to predict CN. These techniques have been used due to the simplicity and ease associated with model development and application.…”

Section: Introductionmentioning

confidence: 99%

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Jameel

Oudenhoven

Naser

et al. 2021

SAE Int. J. Fuels Lubr.

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Artificial intelligence based computing systems like artificial neural networks (ANN) have recently found increasing applications in predicting complex chemical phenomena like combustion properties. The present work deals with the development of an ANN model which can predict the derived cetane number (DCN) of oxygenated fuels containing alcohol and ether functionalities. Experimental DCN's of 499 fuels comprising of 116 pure compounds, 222 pure compound blends, and 159 real fuel blends were used as the dataset for model development. DCN measurements of sixty new fuels were carried out in the present work and the data for the rest was collected from the literature. Fuel chemical composition expressed in the form of eight functional groups namely paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic -CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, alcoholic OH groups and ether O groups, along with two structural parameters namely, molecular weight and branching index (BI), were used as the ten input features of the model. The qualitative and quantitative determination of functional groups present in real fuels was performed using 1 H nuclear magnetic resonance (NMR) spectroscopy. A robust ANN methodology was followed to prevent over fitting using a multilevel grid search and genetic algorithm. The final developed model with two hidden layers was tested with 15 % of randomly generated unseen points from the dataset and a regression coefficient (R 2 ) of 0.992 was observed between the experimental and predicted DCN values. An average absolute error of 0.91 obtained from the test set indicates that the developed ANN model is successful in predicting the DCN of oxygenated fuels and captures the dependence of the fuel's ignition quality (i.e. DCN) on its constituent functional groups.

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Section: Introductionmentioning

confidence: 99%

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Jameel

Oudenhoven

Naser

et al. 2021

SAE Int. J. Fuels Lubr.

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“…The biomass feedstock consists of cellulose, hemicellulose, and lignin, which makes new production pathways possible. As the fuels help to establish highly efficient and "clean" combustion, it is expected that the new fuels will differ from today's ones as shown by Leitner et al 1 For instance, the currently investigated renewable fuels contain oxygen atoms because they are made from biomass which was shown by Hoppe et al 2 To find suitable fuel molecules, a tool based on quantitative structure−property relationships was developed by Hechinger et al, 3 which initially generates all possible molecular structures according to valence rules and given restrictions (e.g., number of carbon atoms) developed by Dahmen et al 4 For gasoline-like fuels, the restrictions are as follows. A derived cetane number below 9, a heating value of >30 MJ/kg, a boiling point between 50 and 100 °C, and an enthalpy of vaporization of <60 kJ/kg air,λ=1 were selected by Hoppe et al 5 These parameters are discussed in more detail in the study by Hoppe et al 2 In that prior work, the selection of fuels is also more thoroughly explained.…”

Section: Introductionmentioning

confidence: 99%

“…To find suitable fuel molecules, a tool based on quantitative structure–property relationships was developed by Hechinger et al, which initially generates all possible molecular structures according to valence rules and given restrictions (e.g., number of carbon atoms) developed by Dahmen et al For gasoline-like fuels, the restrictions are as follows. A derived cetane number below 9, a heating value of >30 MJ/kg, a boiling point between 50 and 100 °C, and an enthalpy of vaporization of <60 kJ/kg air,λ=1 were selected by Hoppe et al These parameters are discussed in more detail in the study by Hoppe et al In that prior work, the selection of fuels is also more thoroughly explained.…”

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Study of Abnormal Combustion in Direct Injection Spark Ignition Engines Using Conventional and Alternative Fuels

et al. 2019

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In this paper, the potential of alternative fuels to reduce preignition in engines is primarily investigated. Two ketones (2-butanone and 3-methylbutanone), a furan (2-methylfuran), and five alcohols (ethanol, iso-propanol, 2-butanol, iso-butanol, and 1-propanol) are compared to three conventional fuels (RON95E0, RON95E10, and iso-octane). If the alternative fuels can reduce preignition significantly, the efficiency of spark-ignition engines can be potentially increased. One major aim is to categorize the fuels in terms of preignition resistance. Additionally, the distributions of the initial preignition kernels are measured by high-speed luminescence imaging to analyze the reason for preignition. Furthermore, the occurrence of measured preignition is compared with computed ignition delay times at the pressures and temperatures of the engine load points used to find out whether the mixture is prone to autoignition in the bulk gas phase. This is most likely the case for the three conventional fuels because of thermodynamically critical gas-phase conditions. Seemingly, preignition is not induced by droplets, presumably because of an improved injector targeting. Moreover, the preignition resistance of the alternative fuels is significantly higher (critical intake pressure difference: ∼2 bar) compared to that of the three conventional fuels. It can also be concluded that 2-butanone is most resistant against preignition. Similarly, alcohols and 3-methylbutanone are highly beneficial compared to conventional fuels. Overall, the most important fuel properties for preignition appear to be research octane number and enthalpy of vaporization. Apparently, ignition is caused by glow ignition at a hot surface (spark plug) only for 2-methylfuran, iso-butanol, and 1-propanol.

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“…Since the biomass conversion can be highly efficient, new production pathways including new technologies, such as selective catalysis and ionic liquids, are utilized. [1][2][3][4] Moreover, the fuels can contribute to a highly efficient and clean combustion by their chemical and physical characteristics. For this reason, the new fuels will differ significantly from today's fuels, which are based on fossil sources.…”

Section: Introductionmentioning

confidence: 99%

Combustion and emission behavior of linear C₈-oxygenates

Heuser

Mauermann

Wankhade

et al. 2015

International Journal of Engine Research

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Alternative fuels have become of great importance in order to secure a sustainable mobility within the next decades. Within the Cluster of Excellence, ''Tailor-Made Fuels from Biomass'' at RWTH Aachen University, several possible fuel candidates could be derived from (hemi-)cellulose by selective catalytic conversion. The proposed fuel candidates include furans, ethers, alcohols, and ketones. Experiments with the isomers di-n-butyl ether and 1-octanol have proven their suitability for diesel-type combustion. With di-n-butyl ether being prone to auto-ignition, overall hydrocarbon, carbon monoxide, and soot emissions are reduced compared to diesel. In contrast, the prolonged ignition delay with 1-octanol causes an increase in HC and CO emissions particularly at low engine loads. However, soot emissions are even below those of di-n-butyl ether. With regard to particulate matter, an Exhaust Emissions Particulate Sizer Spectrometer (EEPS ä ) has been utilized to investigate the particle size or number distribution. Compared to diesel, a reduction of the total particle number up to 80% was seen with the oxygenates next to a shift toward reduced particle mobility diameter. The HC emissions of both di-n-butyl ether and 1-octanol have been studied in detail by means of gas chromatographymass spectrometry. As a main result, not only the general emission reduction potential of the biofuel alternatives 1-octanol and di-n-butyl ether can be shown with this work. Gas chromatography-mass spectrometry revealed that the composition of hydrocarbons emitted with the C 8 -oxygenates is almost equal to those with diesel, except for the unburned fuel that is present in the exhaust gas. Quantification showed that the carcinogenic component 1,3-butadiene increased with the alternative fuel candidates, whereas particularly benzene and ethyl benzene reduced. Since both di-n-butyl ether and 1-octanol are found in high proportions in the exhaust gas, the effects on the aftertreatment system have to be investigated in a subsequent campaign.

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Towards Model-Based Identification of Biofuels for Compression Ignition Engines

Cited by 24 publications

References 44 publications

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Experimental and Numerical Study of Abnormal Combustion in Direct Injection Spark Ignition Engines Using Conventional and Alternative Fuels

Combustion and emission behavior of linear C₈-oxygenates

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Towards Model-Based Identification of Biofuels for Compression Ignition Engines

Cited by 24 publications

References 44 publications

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Experimental and Numerical Study of Abnormal Combustion in Direct Injection Spark Ignition Engines Using Conventional and Alternative Fuels

Combustion and emission behavior of linear C8-oxygenates

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Product

Resources

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Combustion and emission behavior of linear C₈-oxygenates