Hydrotreating Modeling - Helping Refiners to Face Challenges of the Future
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Résumé
Hydrotreating Modeling - Helping Refiners to Face Challenges of the Future Jinwen Chen; Jinwen Chen Natural Resources Canada Search for other works by this author on: This Site Google Scholar Hong Yang; Hong Yang Natural Resources Canada Search for other works by this author on: This Site Google Scholar Zbigniew Ring Zbigniew Ring The National Centre for Upgrading Technology Search for other works by this author on: This Site Google Scholar Paper presented at the SPE International Thermal Operations and Heavy Oil Symposium, Calgary, Alberta, Canada, November 2005. Paper Number: SPE-97735-MS https://doi.org/10.2118/97735-MS Published: November 01 2005 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Chen, Jinwen, Yang, Hong, and Zbigniew Ring. "Hydrotreating Modeling - Helping Refiners to Face Challenges of the Future." Paper presented at the SPE International Thermal Operations and Heavy Oil Symposium, Calgary, Alberta, Canada, November 2005. doi: https://doi.org/10.2118/97735-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE International Thermal Operations and Heavy Oil Symposium Search Advanced Search AbstractIn order to help heavy oil upgraders and petroleum refineries optimize hydrotreater performance, a predictive hydrotreating process model is being developed to eventually predict the quality of the hydrotreated products under certain operating conditions. To establish this model, a number of important issues have been addressed and this paper summarizes the research results pertaining to these issues.1. IntroductionCurrent environmental regulations require production of ultra-low sulphur diesel (ULSD) in the near future. In the US and Canada sulphur content in on-road diesel has to be reduced from 500 ppm to 15 ppm by 2007 [1, 2]. In most European countries and some other developed countries, similar or even tougher regulations on sulphur content in diesel fuels will be implemented. Such stringent specifications create a serious challenge to refineries. Process modeling and simulation is essential to both new hydrotreater design and existing hydrotreater revamping/retrofitting. Predicting hydrotreater performance in ultra-low sulphur operation mode, with various feedstocks and process operating conditions, is one of the most important and difficult challenges refiners are facing [3,4].A hydrotreating process model is currently being developed at the National Centre for Upgrading Technology (NCUT) to optimize hydrotreater performance, and in the longer term, to provide a predictive tool to facilitate hydrotreater design. This model, when completed, will not only predict the product yield, major reactants conversion, and hydrogen consumption, but also the product quality (such as density, viscosity, cetane number, and sulphur and nitrogen contents, etc.) of the individual fractions of the total hydrotreated liquid product, given a detailed characterization of the feedstock, unit configuration, and operating conditions. To achieve this goal, research work has been and is still being conducted in the following areas: 1) characterization, sulphur and nitrogen speciation of petroleum fractions; 2) product quality modeling; 3) detailed kinetics study of hydrodesulphurization (HDS); 4) molecular representation of petroleum feedstocks; and 5) vapor-liquid phase equilibrium under commercial hydroprocessing conditions and its effect on HDS.This paper summarizes the key research activities in the above-mentioned areas, and presents some typical experimental and computational results, focusing on HDS kinetics studies.2. Characterization, Sulphur and Nitrogen Speciation of Petroleum FractionsNaturally, physical properties and product quality of petroleum fractions - such as density, viscosity, cetane number - are highly correlated to the fraction's chemical composition (hydrocarbon type distribution). In order to model and simulate HDS reaction kinetics in a hydrotreater operated under ULSD conditions, it is necessary to know the required peak-by-peak speciation of sulphur and nitrogen compounds. A number of characterization methods have been developed to provide information on by-boiling-point distributions of hydrocarbon types, and sulphur and nitrogen speciation in middle distillates. Brief descriptions of these methods follow.PIONA (Paraffin-Isoparaffin-Olefin-Naphthene-Aromatics): PIONA analysis provides compositional distribution of paraffins, isoparaffins, olefins, naphthenes and aromatics by carbon number (from 3 to 11) in the boiling range of IBP-200°C.GC-MS (Gas Chromatography-Mass Spectrometry): The oil sample is first separated into saturate, olefinic, aromatic, polar and ashphaltenic fractions by solid phase extraction (SPE) or open column chromatography (SARA). The saturate and the aromatic fractions are analyzed by GC-MS method and the olefin and polar fractions are quantified with GC-FID. In both analyses, the quantitative calculations are performed from 200°C to 540°C, giving by-boiling-point distribution of various hydrocarbon types in saturates, aromatics, polars, asphaltenes and olefins. Keywords: heavy oil upgrading, petroleum feedstock, hydrodesulphurization, effectiveness factor, product quality, downstream oil & gas, characterization matrix, artificial intelligence, fraction, catalyst Subjects: Processing Systems and Design, Fluid Characterization, Heavy oil upgrading This content is only available via PDF. 2005. SPE/PS-CIM/CHOA International Thermal Operations and Heavy Oil Symposium You can access this article if you purchase or spend a download.
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
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