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Record W4237914156 · doi:10.2523/97735-ms

Hydrotreating Modeling - Helping Refiners to Face Challenges of the Future

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

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsCitationOil refineryComputer scienceLibrary scienceOperations researchEngineeringWaste management

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.230

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.222
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2005
Admission routes2
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