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Record W2334262718 · doi:10.1021/ie5005042

Improved Synthesis of Hydrogen Networks for Refineries

2014· article· en· W2334262718 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsUniversity of Waterloo
FundersUniversity of WaterlooNational University of Singapore
KeywordsRefineryComputer scienceOil refineryBilinear interpolationGas compressorMathematical optimizationKey (lock)Work (physics)Process engineeringNonlinear systemInteger programmingNonlinear programmingAlgorithmEngineeringWaste managementMathematicsMechanical engineering

Abstract

fetched live from OpenAlex

Hydrogen supplies constitute a significant cost for refineries. Thus, managing hydrogen flows and consumption in an integrated and cost-effective manner is critical. This work presents a systematic framework for modeling key units in a refinery hydrogen network. It proposes an improved superstructure and a simpler mixed-integer nonlinear programming model for synthesizing such a network with minimum total annualized cost. In contrast to the existing literature, it allows dedicated compressors, realistic cost correlations, temperature effects, stream-dependent properties, fuel gas specifications, heating, cooling, and valve expansions. Furthermore, it avoids the many bilinear and posynomial terms present in the existing models; thus it is easier to solve. Our tests with several literature examples confirm that our model gives better and more realistic solutions than the previous models, and it is also suitable for retrofit synthesis.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.403
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.045
GPT teacher head0.283
Teacher spread0.238 · 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