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Record W2008297889 · doi:10.1002/cjce.5450780406

Novel reactor for cyanide solution treatment

2000· article· en· W2008297889 on OpenAlexaffvenue
Luc Fortin, Vijaya K. Kasireddy, Gervais Soucy, Jean‐Luc Bernier

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCyanideChemistryPlasmaThermal decompositionDecompositionLeachatePyrolysisAtmospheric pressureBatch reactorPhotocatalysisPlug flow reactor modelKineticsChemical engineeringContinuous stirred-tank reactorMaterials scienceInorganic chemistryCatalysisEnvironmental chemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract This paper presents the treatment of cyanide solution being derived from the aluminum industry by direct contact with thermal plasma in a novel reactor. The energy is provided by a plasma submerged in the solution which allows direct contact between the plasma and the solution. The scope of this study was to determine the feasibility of treating free and complex cyanides in solution through bench‐scale operations. An innovative reactor was designed and fabricated to provide stable operating conditions. Many parameters were studied, such as the NaOH concentration (32 and 57 g/L), initial SPL (Spent PotLining) leachate concentration (154 and 354 mg/L), plasma power (10 and 19 kW) and relative reactor pressure (0 and 1.34 MPa). These experiments allowed us to evaluate the kinetics of complex cyanide decomposition under thermal plasma conditions. At atmospheric pressure (about 100°C), the rate of cyanide decomposition was 12 times greater than that of thermal hydrolysis occurring in a plug flow reactor at the same temperature. These improvements are attributed to the presence of both steep thermal gradients and reaction photocatalysis by the plasma UV radiation.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.752

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 head0.024
GPT teacher head0.206
Teacher spread0.181 · 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

Citations17
Published2000
Admission routes2
Has abstractyes

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