Heavy fuel oil pyrolysis and combustion: Kinetics and evolved gases investigated by TGA-FTIR

Jameel, Abdul Gani Abdul; Han, Yunqing; Brignoli, Omar; Telalović, Selvedin; Elbaz, Ayman M.; Im, Hong G.; Roberts, William L.

doi:10.1016/j.jaap.2017.08.008

Cited by 95 publications

(29 citation statements)

References 54 publications

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“…The proximate analysis was conducted following the procedure presented by Haines [33] using a thermogravimetric analyzer (TGA). The proximate analysis of VGO is being in line with other previously studied liquid petroleum as vacuum residue (VR), [34,35] heavy fuel oil, [36] and crude oils [37] . For comparison, the proximate and ultimate analysis of glucose as well as other biomass and carbonaceous materials are also reported in Table 1.…”

Section: Methodssupporting

confidence: 57%

Apparent Kinetics of Co‐Gasification of Biomass and Vacuum Gas Oil (VGO)

Al‐Attas

Lucky

Hossain

2021

Chemistry An Asian Journal

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This communication reports the beneficial effects of co‐gasification of biomass and residual oil to produce syngas. In this regard, various blends of glucose (a biomass surrogate) to vacuum gas oil (VGO) have been employed to investigate the synergic effects on the gasification process. The non‐isothermal co‐gasification experiments were conducted in a thermogravimetric analyzer at different heating rates and gasifying agents. The analysis showed that the co‐gasification rate increased with the increase of glucose content in the feedstock. The presence of the oxygen in the biomass molecules helped the overall gasification process. The maximum gasification rate of 42.70 wt/min (DTGmax) was observed with 25 wt% glucose containing sample. The use of gasifying agents appeared to have some influence, especially during high temperature gasification of the glucose‐VGO blends. At a same gasification temperature, the co‐gasification rate of glucose‐VGO blends were found to be 125.7 wt/min and 98.59 wt%/min for N2 and CO2, respectively. The kinetics of the co‐gasification of glucose‐VGO blends was conducted based on modified random pore model using TGA experimental data and implemented in MATLAB. The estimated activation energy and rate constants were found to be consistent to the observed co‐gasification rates. The apparent activation energies of co‐gasification of VGO/biomass blends with different weight percentages shows values ranging 60.56–48.25 kJ/mol. The kinetics analysis suggested that the addition of biomass helped to increase the reaction rate by lowering the activation energy required for accomplishing the reactions compared with petroleum carbonaceous feedstocks. The reaction rate constants isotherms are plotted to show that the k‐values are exhibiting similar trends at moderate heating rates between 20 and 60 °C/min. This remark arises due to the nature of the reactions involved which are considered to be inherently similar in this range of heating rate.

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Section: Methodssupporting

confidence: 57%

Apparent Kinetics of Co‐Gasification of Biomass and Vacuum Gas Oil (VGO)

Al‐Attas

Lucky

Hossain

2021

Chemistry An Asian Journal

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“…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. Therefore, expressing a fuel's chemical composition by means of functional groups enables us to capture its chemical nature and predict its DCN.…”

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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“…The studies about the SCW technique for shale rock require the selection of a method that can provide knowledge about the converted products' structure and composition. One of the informative instrumental methods is considered the FTIR spectroscopy, which allows for the evaluation of transformations not only in various hydrocarbons, but also in functional groups, at natural and technology-related processes [42,43]. The IR spectra of the obtained hydrocarbon groups are presented in Figure 5.…”

Section: Resultsmentioning

confidence: 99%

Heavy Oil Hydrocarbons and Kerogen Destruction of Carbonate–Siliceous Domanic Shale Rock in Sub- and Supercritical Water

et al. 2020

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This paper discusses the results of the influences of subcritical (T = 320 °C; P = 17 MPa) and supercritical water (T = 374 °C; P = 24.6 MPa) on the yield and composition of oil hydrocarbons generated from carbonaceous–siliceous Domanic shale rocks with total organic content (Corg) of 7.07%. It was revealed that the treatment of the given shale rock in sub- and supercritical water environments resulted in the decrease of oil content due to the intensive gas formation. The content of light hydrocarbon fractions (saturated and aromatic hydrocarbons) increased at 320 °C from 33.98 to 39.63%, while at 374 °C to 48.24%. Moreover, the content of resins decreased by almost twice. Insoluble coke-like compounds such as carbene–carboids were formed due to decomposition of kerogen after supercritical water treatment. Analysis of oil hydrocarbons with FTIR method revealed a significant number of oxygen-containing compounds, which are the hydrogenolysis products of structural fragments formed after destruction of kerogen and high-molecular components of oil. The gas chromatography–mass spectroscopy (GC–MS) method was applied to present the changes in the composition of mono- and dibenzothiophenes, which indicate conversion of heavy components into lighter aromatic hydrocarbons. The specific features of transforming trace elements in rock samples, asphaltenes, and carbene–carboids were observed by using the isotopic mass-spectrometry method.

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Heavy fuel oil pyrolysis and combustion: Kinetics and evolved gases investigated by TGA-FTIR

Abstract: AM, et al. (2017) Heavy fuel oil pyrolysis and combustion: kinetics and evolved gases investigated by TGA-FTIR.

Cited by 95 publications

References 54 publications

Apparent Kinetics of Co‐Gasification of Biomass and Vacuum Gas Oil (VGO)

Apparent Kinetics of Co‐Gasification of Biomass and Vacuum Gas Oil (VGO)

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Heavy Oil Hydrocarbons and Kerogen Destruction of Carbonate–Siliceous Domanic Shale Rock in Sub- and Supercritical Water

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