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Record W2333943581 · doi:10.1021/ie4043938

Safe-Parking of a Hydrogen Production Unit

2014· article· en· W2333943581 on OpenAlexafffund
Miao Du, Prashant Mhaskar, Yu Zhu, Jesus Flores‐Cerrillo

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsProduction (economics)Hydrogen productionHydrogenUnit (ring theory)Environmental scienceComputer scienceProcess engineeringChemistryEngineeringMathematicsOrganic chemistryEconomics

Abstract

fetched live from OpenAlex

This work considers the problem of handling the failure of purification equipment in a hydrogen production process. In this process, natural gas and superheated steam are fed to a heated chemical reactor termed a reformer to produce hydrogen. The effluent gas is further processed and finally purified in a pressure swing adsorber (PSA). The off-gas out of the PSA is used to provide heat to the reformer. The failure of the PSA results in the loss of the off-gas, precluding the possibility of the continuation of nominal operation. If not properly handled, this fault can lead to a shutdown of the entire plant. To achieve stable operation while meeting operating requirements, a model predictive control (MPC) based safe-parking framework is designed for the handling of the fault. The key idea is to drive the process to a feasible operating point that enables stable operation in the faulty mode. MPC is used to handle the multivariable nature of the process and operating constraints. It also guides the process to a different operating region while meeting operating requirements. The effectiveness of the safe-parking design is demonstrated through simulations using a first-principles model of the hydrogen production unit.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.094
GPT teacher head0.299
Teacher spread0.204 · 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 designBench or experimental
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

Citations5
Published2014
Admission routes2
Has abstractyes

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