A new method for determining average boiling points of oils using a thermogravimetric analyzer

Rannaveski, Rivo; Järvik, Oliver; Oja, Vahur

doi:10.1007/s10973-016-5612-6

Cited by 18 publications

(26 citation statements)

References 18 publications

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“…Hence, the true boiling point (TBP) curves of the oil samples was obtained by using a Thermogravimetric Analyzer, TGA (TG 209 F1 Iris ® instrument, NETZSCH-Geratebau GmbH, Selb, Germany), with the temperature ramped from 25 to 345 • C, at a heating rate of 10 • C/min, and an isothermal 345 • C for 5 min, to ensure complete vaporization of the middle distillate fractions under nitrogen flowrate of 40 mL/min. The measured weight loss due to evaporation of light and middle distillate fractions as the sample is heated in an inert atmosphere (N 2 ), gives similar data to distillation curve obtained using ASTM method D86 [28,37], and also with SimDist using ASTM method D2892 [28]. However, beyond 350 • C, complex cracking reactions begins to occur [38].…”

Section: Analytical Techniquessupporting

confidence: 59%

“…In this study, the volatiles were lumped with the naphtha fractions (IBP-177 • C), middle distillate fractions (177-345 • C) and residue greater than 345 • C. This classification provides a more in-depth compositional change that occurred due to upgrading reactions. While the mass percentage loss curve obtained with TGA gives the boiling point distribution of the vaporized components particularly established on the remaining oil in the crucible, in reality, the TBP curve with distillation gives what could be regarded as a condensation curve [28]. It has been reported that the data obtained with TGA present a good correlation factor of 95% at 5% and 10% amount distilled from SimDist ASTM method D2892 [29,30].…”

Section: Distillation and Distillate Fractionsmentioning

confidence: 99%

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Tetralin and Decalin H-Donor Effect on Catalytic Upgrading of Heavy Oil Inductively Heated with Steel Balls

et al. 2020

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The Toe-to-Heel Air Injection (THAI) combined with catalytic upgrading process in situ (CAPRI) has demonstrated it can simultaneously extract and upgrade heavy oil in situ. This paper reports the investigation of augmenting temperature deficit and suppressing coke formation in the CAPRI section through the incorporation of induction heating and H-donor solvents. An induction-heated catalytic reactor was designed and developed, heated with steel balls in a mixed bed of NiMo/Al2O3 catalyst (66% v/v) to 425 °C temperature, 15 bar pressure and 0.75 h−1 LHSV (Liquid Hourly Space Velocity). The catalyst surface area, pore volume and pore size distribution were determined by using nitrogen adsorption–desorption, while the location of coke deposits within the microstructure of the pelleted spent catalyst was analyzed with X-ray nano-Computed Tomography (X-ray nano-CT). Findings showed that induction heating improved the catalyst performance, resulting in a 2.2° American Petroleum Institute (API) gravity increase of the upgraded oil over that achieved with the conventional heating method. The increment in API gravity and viscosity reduction in the upgraded oils with nitrogen gas only, N2 and H-donor solvents, and hydrogen gas environments can be summarized as follows: decalin > H2 gas >= tetralin > N2 gas. Meanwhile, the improvement in naphtha fraction, middle distillate fractions and suppression of coke formation are as follows: decalin > H2 gas > tetralin > N2 gas. The X-ray nano-CT of the spent catalyst revealed that the pellet suffers deactivation due to coke deposit at the external surface and pore-mouth blockage, signifying underutilization of the catalyst interior surface area.

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Section: Analytical Techniquessupporting

confidence: 59%

Section: Distillation and Distillate Fractionsmentioning

confidence: 99%

Tetralin and Decalin H-Donor Effect on Catalytic Upgrading of Heavy Oil Inductively Heated with Steel Balls

et al. 2020

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“…The method used for measuring the average boiling points of the samples was developed in our laboratory and is described in detail by Rannaveski et al [54]. With this method the sample is placed in a small metal pan that is then closed with a lid.…”

Section: Average Boiling Pointmentioning

confidence: 99%

Predicting Fuel Properties from Infrared Spectra

Baird¹

2017

Preprint

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Because experimentally measuring fuel properties is usually time consuming and costly, property values are often estimated using correlations. And yet, even when predicting these values input data must still be measured. This makes methods based on infrared spectra an attractive alternative because infrared spectra can be measured quickly and continuously, even within an existing process.Although fuel properties have long been predicted from infrared spectra, the focus has been almost entirely on quality parameters of the fuel. Instead, this research focused specifically on determining the properties that are needed for modeling a fuel’s behavior in industrial processes and in the environment. In this work the predictive models based on infrared spectra were created using machine learning techniques. Support vector regression was the main method used. Models were created for predicting 11 different properties: specific gravity, the refractive index parameter, average boiling point, average molecular weight, carbon content, hydrogen content, sulfur content, hydroxyl group content, pour point, the thermal expansion coefficient and viscosity. Experimental data for creating the models were measured as part of a larger project.The performance of the infrared models was compared to that of conventional bulk (or average) property correlations commonly used. Predictions based on infrared spectra had a comparable or better accuracy for all the properties compared, except viscosity. This shows that predictive methods based on infrared spectra can perform well enough to be used as a substitute for conventional predictive methods. Further analysis also indicated that the main factor limiting the accuracy of the models was the accuracy of the experimental data used in regression.Because many important fuel properties vary with temperature or pressure, and predicting properties at different temperatures was also investigated. To model temperature dependence, the coefficients of an algebraic equation were found using infrared spectra. The results showed that the models obtained can be used to predict temperature dependent properties over a wide range of temperatures, which allowed density and viscosity to be predicted with accuracies comparable to the models for predictions at a single temperature.An eventual goal would be to predict parameters for equations of state from infrared spectra. As the results presented here indicate, this is possible. However, limitations discussed in this work need to be taken into account.

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“…To determine experimentally average boiling points of pre-prepared narrow boiling range fractions a recently developed experimental method was used. The method is based on thermogravimetric analysis (TGA) and requires only a small amount of sample, about 20 mg [31]. Using this technique it was convenient to determine weight average boiling points of oils with narrow boiling ranges in a fast manner [23].…”

Section: Average Boiling Point Determination By Tgamentioning

confidence: 99%

“…Using this technique it was convenient to determine weight average boiling points of oils with narrow boiling ranges in a fast manner [23]. The average deviation of the normal boiling points was evaluated to be 1.2 °C (absolute average deviation 2.1 °C) [31]. The method is based on the measurement principle and procedure underlying the ASTM E1782 standard "Standard Test Method for Determining Vapor Pressure by Thermal Analysis" [32].…”

Section: Average Boiling Point Determination By Tgamentioning

confidence: 99%

Astm D86 Distillation in the Context of Average Boiling Points as Thermodynamic Property of Narrow Boiling Range Oil Fractions

Rannaveski¹,

Listak²,

Oja³

2018

Oil Shale

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The average boiling points (ABPs) of narrow boiling range oil distillation cuts are important in predicting thermodynamic and physical properties of oils. Due to convenience, simple batch distillation methods, either at atmospheric or reduced pressure, are often used to separate shale oils into fractions, including narrow boiling range fractions, and it has been attempted to calculate average boiling points directly from the distillation data. Using wide industrial shale oil fractions from Estonian kukersite oil shale and based on ASTM "Standard Test Method for Distillation of Petroleum Products at Atmospheric Pressure" (ASTM D86), this paper is aimed to find out how much the average boiling points determined directly from distillation, as an arithmetic average of the initial and final temperatures of the thermometer during fractions collection, differ from actual average boiling points (AABPs). The actual average boiling points of narrow boiling range oil fractions, pre-prepared by the same ASTM D86 distillation, were measured afterwards using a recently developed thermogravimetric analysis (TGA) based experimental method, which requires only about 20 mg of sample. The study indicated that AABPs were always lower than the respective average values determined directly from ASTM D86 distillation data.

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A new method for determining average boiling points of oils using a thermogravimetric analyzer

Cited by 18 publications

References 18 publications

Tetralin and Decalin H-Donor Effect on Catalytic Upgrading of Heavy Oil Inductively Heated with Steel Balls

Tetralin and Decalin H-Donor Effect on Catalytic Upgrading of Heavy Oil Inductively Heated with Steel Balls

Predicting Fuel Properties from Infrared Spectra

Astm D86 Distillation in the Context of Average Boiling Points as Thermodynamic Property of Narrow Boiling Range Oil Fractions

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