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Enregistrement W4237914156 · doi:10.2523/97735-ms

Hydrotreating Modeling - Helping Refiners to Face Challenges of the Future

2005· article· en· W4237914156 sur OpenAlexaffabout
Jinwen Chen, Hong Yang, Zbigniew Ring

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEngineering
ThématiqueCatalysis and Hydrodesulfurization Studies
Établissements canadiensNatural Resources Canada
Organismes subventionnairesnon disponible
Mots-clésCitationOil refineryComputer scienceLibrary scienceOperations researchEngineeringWaste management

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,143
Score d'incertitude au seuil0,230

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,017
Tête enseignante GPT0,222
Écart entre enseignants0,205 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2005
Routes d'admission2
Résumé présentoui

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