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Record W2515987215 · doi:10.1021/acs.iecr.6b01108

Crude-Oil Operations under Uncertainty: A Continuous-Time Rescheduling Framework and a Simulation Environment for Validation

2016· article· en· W2515987215 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2016
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsUniversity of Alberta
FundersMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsComputer scienceRobustness (evolution)Consistency (knowledge bases)Crude oilSet (abstract data type)Operations researchEngineeringArtificial intelligencePetroleum engineering

Abstract

fetched live from OpenAlex

This work presents a novel rescheduling framework of the crude-oil operations based on a continuous-time representation. Abnormal events and uncertainties in the crude-oil tank farm area are considered and analyzed in this framework to improve the robustness of the final plan. A rescheduling model is proposed to handle the various uncertainties. Some managerial experiences and consistency rules can be set in the model for different needs and scenarios. A multiagent based simulator is developed as the validation part with uncertainties to simulate the real-world operations and test the optimization results. The goal of the rescheduling framework is providing a feasible and flexible robust plan and dealing with the disruptive events and uncertainties at the same time. The results of the case studies indicate our framework can support dynamic optimization of crude-oil operations under complex real-world environments.

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.

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)0.001
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: none
Teacher disagreement score0.509
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.058
GPT teacher head0.315
Teacher spread0.256 · 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