Probing the Chemical Kinetics of Minimalist Functional Group Gasoline Surrogates

Ilies, Bogdan Dragos; Khandavilli, Muralikrishna; Yang, Li; Kukkadapu, Goutham; Wagnon, Scott W.; Jameel, Abdul Gani Abdul

doi:10.1021/acs.energyfuels.0c02815

Cited by 16 publications

(5 citation statements)

References 61 publications

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“…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. This approach called as the minimalist functional group (MFG) approach has been comprehensively tested on a number of gasoline, diesel and jet fuels [69,70,73]. A number of other studies [21,22,64,[74][75][76][77][78] reported in the literature have also hinted at the ability of functional groups to reproduce fuel behavior.…”

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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“…Analysis of these functional groups indicates the thermal decomposition reactions occurring during pyrolysis, and this information could help optimize gasifier conditions such that high-value gases could be obtained while minimizing emissions. The functional groups present in a fuel often dictate its properties, − and the evolved groups dictate the propensity to form soot. ,, The IR band assignments for the aforementioned functional groups are provided in Table including the selected wavenumber for each functional group.…”

Section: Resultsmentioning

confidence: 99%

Pyrolysis and Oxidation of Waste Tire Oil: Analysis of Evolved Gases

et al. 2022

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Valorization of waste such as waste tires offers a way to manage and reduce urban waste while deriving economic benefits. The rubber portion of waste tires has high potential to produce pyrolysis fuels that can be used for energy production or further upgraded for use as blend fuel with diesel. In the preset work, waste tire oil (WTO) was produced from the pyrolysis of waste tires in an electric heating furnace at 500–550 °C in the absence of oxygen. Pyrolysis (in nitrogen) and oxidation (in air) of the obtained WTO sample were then performed in a thermogravimetric (TG) furnace that was connected to a Fourier transform infrared cell where the evolved gases were analyzed. The WTO sample was heated up to 800 °C in the TG furnace where the temperature of the sample was ramped up at three heating rates, namely, 5, 10, and 20 °C/min. The TG mass loss and differential thermogravimetric mass loss plots were used to analyze the thermal degradation pathways. Kinetic analysis was performed using the distributed activation energy model to estimate the activation energies along the various stages of the reaction. The pollutant gases, namely, CO 2 , CO, NO, and H 2 O, formed during WTO oxidation were evaluated by means of the characteristic infrared absorbance. The functional groups evolved during pyrolysis, namely, alkanes, alkenes, aromatics, and carbonyl groups, were also analyzed. The obtained information can be used for the better design of gasifiers and combustors, to ensure the formation of high-value gaseous products while reducing the emissions. The utilization of waste tires by producing pyrolysis oils thus offers a way of tackling the menace of waste tires while acting as a potential energy source.

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“…Petroleum gasoline is composed of paraffin, olefins, naphthene, and aromatic compounds that can be disassembled into a finite number of functional groups which define the fuel’s properties. The functional groups in fuel have been used to predict the cetane number of diesel fuel and the sooting propensity of a wide class of neat compounds and also to formulate surrogates for gasoline, diesel, and jet fuels. − Along with functional groups, two additional structural parameters called branching index (BI) and molecular weight (MW) have been included as input features in the ANN model. After each generated mixture is converted into the input features (i.e., functional groups, BI and MW), the ANN model predicts the RON and MON of each.…”

Section: Computational Methodologymentioning

confidence: 99%

A Methodology for Designing Octane Number of Fuels Using Genetic Algorithms and Artificial Neural Networks

et al. 2022

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Refineries often blend fuels in order to achieve mandated fuel properties based on international and domestic standards. One of the mandated properties for gasoline is Octane Number (ON). There are two forms of measuring ON: Research Octane Number (RON) and Motor Octane Number (MON). Although there have been efforts to optimize the blending operation in refineries, the implementations of advanced machine learning models are limited. The intention behind exploring and enhancing this field is to avoid undesirable scenarios, such as off-specification products and quality giveaways, as these scenarios overburden refineries' operational expenditures. The current work presents an innovative approach to predict the optimum fuel blending mechanism. This is accomplished through utilizing an integrated system composed of genetic algorithms (GA) and artificial neural networks (ANN). In addition, this work presents the polygonal algorithms that govern the optimization process. The system analyzes fuel inputs consisting of pure hydrocarbons, hydrocarbon−ethanol blends, and FACE (fuels for advanced combustion engines) gasoline−ethanol blends. There are ten pure hydrocarbons and six FACE gasoline's that are the base of 243 fuels used. These components are presented as multiple input streams to the blending stage, at which the system computes multiple recipes utilizing the GA. The system derives these recipes by capitalizing on the polygonal method. This method offers a systematic approach to design optimal fuel with the lowest addition of relatively high octane components. The system is evaluated using 243 blends. The produced recipes are designed to meet the desired antiknocking index (AKI). The coefficient of determination (R 2 ) result is 0.99 for RON, MON, and AKI, respectively. Furthermore, the mean absolute error (MAE) values are 1.99, 1.23, and 1.40 for RON, MON, and AKI, respectively. These results signify the success of the integrated system and its significant potential impact in mitigating undesirable blending scenarios.

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Probing the Chemical Kinetics of Minimalist Functional Group Gasoline Surrogates

Cited by 16 publications

References 61 publications

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Pyrolysis and Oxidation of Waste Tire Oil: Analysis of Evolved Gases

A Methodology for Designing Octane Number of Fuels Using Genetic Algorithms and Artificial Neural Networks

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