The influence of chemical composition on ignition delay times of gasoline fractions

Naser, Nimal; Jameel, Abdul Gani Abdul; Emwas, Abdul‐Hamid; Singh, Eshan; Chung, Suk Ho; Sarathy, S. Mani

doi:10.1016/j.combustflame.2019.07.030

Cited by 21 publications

(11 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuels can be also be characterized by their relatively small number of molecular moieties, or functional groups, which dictate the combustion characteristics of the fuel like octane number [66], CN [37], flash point [34], sooting tendencies [20], auto-ignition [67], ignition delay [68] etc. Recent works by Abdul Jameel et al [69,70] and Mueller [71,72], have shown that formulating simple surrogates by mimicking the functional groups in the fuel gives the ability to reproduce various physical, thermochemical and combustion properties of the fuel.…”

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.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The slightly lower value is for ethanol-based micro-emulsion fuel. The reason for this decrease in pressure is due to the presence of H2O molecule that lowers the temperature and pressure inside the combustion chamber [20,21]. In Figure 10, the variation of temperature can be seen at varying crank angles.…”

Section: Resultsmentioning

confidence: 98%

“…The temperature of the engine is highest at lower loads for all types of fuels. This is because at lower loads, the mixture is rich and the fuel supply increases, which results in the increase of its temperature and decreases with an increase in load when the mixture turns more towards the leaner side [21,22].…”

Section: Resultsmentioning

confidence: 99%

Parametric Investigation on Single Cylinder Spark Ignition Engine Fueled Methanol Blends; Water-Based Micro Emulsions and Conventional Gasoline

Qadiri¹,

Pasha²,

Rahman³

et al. 2021

IJHT

View full text Add to dashboard Cite

In this contribution, the investigation conducted on alternative fuels includes methanol 20% blended with gasoline 80% and emulsion-based fuel with the composition of gasoline 80%, ethanol 15%, and H2O 5% are compared with 100% conventional gasoline fuel. These fueled single-cylinders spark ignition engine is studied for checking their performance and emission characteristics as per future emission norms. This work is performed on One-dimensional AVL Boost Simulation Software. The simulations predicted the performance and emission characteristics were far lesser than conventional 100% gasoline. These fuels meet the strict emission regulations of Euro VII. The main purpose of this investigation is to use alternative fuels to improve the performance and emission characteristics of the single- cylinder spark ignition engine and reduce the consumption of fossil fuel reserves. This investigation led to the conclusion that by using methanol 20% in 80% gasoline and micro-emulsion, fuel improves the power, BSFC (brake specific fuel consumption), thermal efficiency and combustion properties of the single-cylinder spark-ignition engine. The CO, HC and NOx emissions were also reduced for alternative fuel than 100% gasoline fuel. The novel water-based emulsion fuel showed the lowest value of NOx emissions as compared to blended 20% methanol with 80% gasoline and 100% gasoline fuel.

show abstract

“…Another ad-vantage of the functional group approach is the ability to predict the YSI of real fuels, which most other models reported in the literature are not designed to do. The functional groups present in hydrocarbon fuels, such as gasoline and diesel, can be identified and quantified using techniques such as nuclear magnetic resonance (NMR) spectroscopy [47,60,61]. The oxygenated functional groups, such as alcohol OH and ether O groups, can be calculated from the blending ratio of oxygenates added to the petroleum fuels.…”

Section: Ann Modelmentioning

confidence: 99%

Predicting Sooting Propensity of Oxygenated Fuels Using Artificial Neural Networks

Jameel¹,

Gani²

2021

Processes

View full text Add to dashboard Cite

The self-learning capabilities of artificial neural networks (ANNs) from large datasets have led to their deployment in the prediction of various physical and chemical phenomena. In the present work, an ANN model was developed to predict the yield sooting index (YSI) of oxygenated fuels using the functional group approach. A total of 265 pure compounds comprising six chemical classes, namely paraffins (n and iso), olefins, naphthenes, aromatics, alcohols, and ethers, were dis-assembled into eight constituent 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. These functional groups, in addition to molecular weight and branching index, were used as inputs to develop the ANN model. A neural network with two hidden layers was used to train the model using the Levenberg–Marquardt (ML) training algorithm. The developed model was tested with 15% of the random unseen data points. A regression coefficient (R2) of 0.99 was obtained when the experimental values were compared with the predicted YSI values from the test set. An average error of 3.4% was obtained, which is less than the experimental uncertainty associated with most reported YSI measurements. The developed model can be used for YSI prediction of hydrocarbon fuels containing alcohol and ether-based oxygenates as additives with a high degree of accuracy.

show abstract

The influence of chemical composition on ignition delay times of gasoline fractions

Cited by 21 publications

References 47 publications

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Parametric Investigation on Single Cylinder Spark Ignition Engine Fueled Methanol Blends; Water-Based Micro Emulsions and Conventional Gasoline

Predicting Sooting Propensity of Oxygenated Fuels Using Artificial Neural Networks

Contact Info

Product

Resources

About