MétaCan
Menu
Back to cohort
Record W1977710956 · doi:10.5539/mas.v4n10p21

Impact of Coagulation Agents on the Performance of Flotation Unit for the Treatment of Industrial Waste Water

2010· article· en· W1977710956 on OpenAlexvenueno aff
Salam J. Bash AlMaliky

Bibliographic record

VenueModern Applied Science · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsAlumBentoniteCoagulationPulp and paper industryWastewaterEffluentEnvironmental scienceEnvironmental engineeringChemistryWaste managementChemical engineeringMedicine

Abstract

fetched live from OpenAlex

The performance of a batch laboratory scale induced air flotation (IAF) unit in controlling fat, oil, and Grease (FOG) and five days biological oxygen demand (BOD5) in effluents of a meats processing & packing industry, was tested using two types of coagulation agents (Alum and Bentonite clay) and three air flow rates. Prior to coagulation, air flow rates of 3 and 5 l/min were the best for having FOG removal efficiencies of 60% and 62%, respectively. Significant improvements in these were measured (94.5% and 96.2% for the mentioned air flow rates respectively) after the addition of 0.5 g/l Alum, while extra 0.2 g/l bentonite clay as coagulation aid had achieved efficiencies of 96.3% and 99.3% for these air flow rates respectively. Also, the addition of 0.5 g/l Alum had improved the BOD5 reduction efficiency from 54% to 65% and from 63% to 71%, for air flow rates of 3 l/min and 5 l/min respectively, as compared with no Alum cases. Higher BOD5 removal efficiencies of 73% and 78% were measured for the two air flow rates respectively, by aiding the coagulation with 0.2 g/l bentonite clay. At least 25% saving of flotation time was proved by the addition of bentonite clay to achieve final results, similar to these with Alum only for both FOG and BOD5, which in turn may save part of the operational costs.Author would like to acknowledge the logistic and technical support of the Ins. of International Education IIE, Scholars Rescue Fund SRF and Russ College of Eng. / OU.

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 score0.185
Threshold uncertainty score0.284

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 studies0.0000.001
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 head0.060
GPT teacher head0.291
Teacher spread0.231 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied ScienceSame topicMembrane Separation TechnologiesFrench-language works237,207