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Record W2529309687 · doi:10.1002/cjce.22708

Analysis for effects of electrolyte level on energy consumption in magnesium electrolysis by finite element method

2016· article· en· W2529309687 on OpenAlexvenueno aff
Ze Sun, Liwei Cai, Chenglin Liu, Guimin Lu, Jianguo Yu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsElectrolyteElectrolysisMagnesiumAnodeCell voltagePolymer electrolyte membrane electrolysisElectrolytic cellVoltageMaterials scienceElectrolytic processChemistryChemical engineeringMetallurgyElectrodeElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

ABSTRACT In the process of magnesium electrolysis, increasing the level of electrolyte can reduce the cell voltage and energy consumption in an electrolysis magnesium cell. In this paper, a three‐dimensional model of a full cell was implemented to study the relationship between the cell voltage and electrolyte level in a magnesium electrolysis cell. Firstly, the resistance voltage of difference current intensity in magnesium electrolysis cells were discussed with the electrolyte depth from 1.25 to 1.45 m. Moreover, the size of anodes and positions of anodes and cathodes were changed to investigate the trend of resistance voltage in the cell. The energy consumption in each part of the electrolyte was integrated to further understand the energy distribution in the electrolyte to explore which part of the electrolyte has the greatest contribution to the cell voltage. Through analyzing components of the cell voltage, energy distribution, and thermal balance, the explanation of the relationship between electrolyte level and energy consumption can be obtained.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.225
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 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
Published2016
Admission routes1
Has abstractyes

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