MétaCan
Menu
Back to cohort
Record W2598132234 · doi:10.1002/cjce.22848

Treatment of wastewater from syngas wet scrubbing: Model‐based comparison of phenol biodegradation basin configurations

2017· article· en· W2598132234 on OpenAlexvenueno aff
Junaid Akhlas, Alberto Bertucco, Fabio Ruggeri, Guido Collodi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsBiodegradationPhenolWastewaterHydraulic retention timeData scrubbingChemistryPulp and paper industrySewage treatmentMicroorganismWaste managementEnvironmental scienceEnvironmental engineeringOrganic chemistryGeologyBacteriaEngineering

Abstract

fetched live from OpenAlex

Abstract A treatment process for the biological removal of phenol from wastewater generated from the wet scrubbing of syngas is discussed with the objective of reducing Hydraulic Retention Time (HRT) for the process. In phenol biodegradation, a major challenge is posed by the inhibitory effect of high inlet phenol concentrations on the functioning of degrading microorganisms, leading to excessively high HRT values. This study provides a theoretical insight on the effect of reactor configuration and operation mode on the residence time for high strength phenolic wastewaters. Two pre‐treated wastewater streams mainly contaminated with phenol, with different flow rates and phenol concentrations, have been considered for evaluation in this work. Various configurations to remove phenol have been studied and compared. The kinetics in the reactors have been estimated using the Haldane‐Andrews equation for a variety of microorganisms. The configurations which require minimum residence time for a given extent of phenol biodegradation are those employing two reactors in series with a certain reflux stream. Gulosibacter sp. YZ4 is found to be the most effective microorganism in removing phenol within the minimum retention period.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.431

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 head0.037
GPT teacher head0.254
Teacher spread0.217 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicOdor and Emission Control TechnologiesFrench-language works237,207