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Record W2132579842 · doi:10.1002/aic.13988

Modeling, Simulation, and Optimization of Hybrid Fe(II)/Fe(III) Redox Flow Fuel Cell System

2012· article· en· W2132579842 on OpenAlexaff
Hossein Hojjati, Kalin Penev, Victor Pupkevich, Dimitre Karamanev

Bibliographic record

VenueAIChE Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsWestern University
Fundersnot available
KeywordsBioreactorFerrousTransient (computer programming)RedoxSteady state (chemistry)Flow (mathematics)ChemistryProcess engineeringChemical engineeringMaterials scienceComputer scienceEngineeringMechanicsMetallurgyInorganic chemistryPhysics

Abstract

fetched live from OpenAlex

The kinetic parameters of ferrous iron oxidation, covering both lag and growth phases at low pH, were determined using a free suspended culture of the bacterium Leptospirillum ferriphilum. A mathematical model was developed to simulate the dynamics of a continuous bioreactor used for operation of a novel hybrid Fe(II)/Fe(III) redox flow fuel cell system. By changing the current load within a predefined range, three runs were performed to predict time‐varying ferrous iron concentration, bacterial cell concentration, and pH as the major output variables of simulation program. The model was experimentally validated through three runs. It was found out that the key variable in dynamic analysis of the bioreactor was the current load applied. To optimize the bioreactor and the fuel cell conditions for a normal‐steady‐state operation, the optimal current profile for a transient phase was determined. A selected optimal policy was also implemented and validated during the mini‐pilot‐scale system experiments. © 2013 American Institute of Chemical Engineers AIChE J, 59: 1844–1854, 2013

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: none
Teacher disagreement score0.719
Threshold uncertainty score0.366

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 head0.015
GPT teacher head0.231
Teacher spread0.215 · 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".

Quick stats

Citations7
Published2012
Admission routes1
Has abstractyes

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