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Record W2047303629 · doi:10.1504/ijogct.2010.037464

Process design, simulation and integration of a new desalter in the crude distillation unit of a refinery

2010· article· en· W2047303629 on OpenAlexaff
Mazda Biglari, Shahrokh Ilkhaani, Ibrahim Alhajri, Ali Lohi

Bibliographic record

VenueInternational Journal of Oil Gas and Coal Technology · 2010
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsUniversity of WaterlooToronto Metropolitan University
Fundersnot available
KeywordsRefineryProcess engineeringRaw materialDistillationCrude oilBarrel (horology)Process (computing)Environmental scienceOil refineryProcess integrationWaste managementEngineeringComputer sciencePetroleum engineeringMechanical engineeringChemistryChromatography

Abstract

fetched live from OpenAlex

This study presents the design and modification of a desalting unit in an existing refinery where crude oil is desalted in a single-stage desalter. The design considers a scheduled increase in the crude feedstock, and change from the current lighter blend to a future heavier blend. A more stringent limit with regard to salt content in the desalted crude is also applied. The simulation studies showed that a second stage electrostatic desalter is required in order to achieve a salt content of one parts per thousand barrel (PTB) compared to the current ten PTB, and starting from a crude oil with 100 PTB. The process was simulated using HYSYS computer software. Calculations related to heat integration of the desalting process to achieve the best operating temperature in the plant are also presented. Mass and heat balances are discussed and several environmental issues are addressed. [Received: October 14, 2009; Accepted: January 13, 2010]

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.294
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations2
Published2010
Admission routes1
Has abstractyes

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