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Record W2093979460 · doi:10.1080/01919510208901622

Comparing Different Designs and Scales of Bubble Columns for Their Effectiveness in Treating Kraft Pulp Mill Effluents

2002· article· en· W2093979460 on OpenAlexaff
Mohamed Gamal El‐Din, Daniel Smith

Bibliographic record

VenueOzone Science and Engineering · 2002
Typearticle
Languageen
FieldEngineering
TopicCoal Combustion and Slurry Processing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsKraft processBubbleMillKraft paperEffluentPulp and paper industryPulp (tooth)Paper millPulp millEnvironmental scienceProcess engineeringWaste managementEngineeringComputer scienceMechanical engineeringMedicine

Abstract

fetched live from OpenAlex

The experimental results obtained in three different types of ozone contactors were analyzed to study the effects of the ozone contactor design, configuration, operating conditions, and scale-up on the: (1) ozonation process induced reduction efficiencies of color, AOX, COD, and TOC from biologically treated Kraft pulp mill effluents; (2) the increase in biodegradability of this type of wastewater; and (3) the dynamics of the ozone gas absorption process. The three types of ozone contactors included: (1) an extra-coarse-bubble diffuser ozone contactor; (2) an impinging-jet ozone contactor; and (3) a fine-bubble diffuser ozone contactor. Similar treatment levels were achieved in those ozone contactors although the impinging-jet bubble column was more effective in treating Kraft pulp mill effluents due to its smaller reactor volume and lower off-gas ozone concentrations. Consequently, the operating costs of an ozonation process and ozone off-gas destruction facilities will be greatly reduced when using the impinging-jet bubble column design for treating Kraft pulp mill effluents.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.228
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2002
Admission routes1
Has abstractyes

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