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Record W2922181006 · doi:10.2166/wqrj.2000.019

Effects of Cement Kiln Dust on pH and Phosphorus Concentrations in an Activated Sludge Wastewater Treatment System

2000· article· en· W2922181006 on OpenAlexaffabout
Moya L. Smith, Christine Campbell

Bibliographic record

VenueWater Quality Research Journal · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsEffluentCement kilnWastewaterChemistryDistilled waterPhosphorusPulp and paper industryActivated sludgeSedimentationCementPhosphateWaste managementEnvironmental chemistryEnvironmental scienceEnvironmental engineeringSedimentChromatographyMaterials scienceMetallurgyGeology

Abstract

fetched live from OpenAlex

Abstract Cement kiln dust (CKD), the waste dust generated from a Portland cement kiln, was evaluated for its ability to increase the pH of acidic processed effluent in activated sludge wastewater treatment. Effluent samples were obtained from Corner Brook Pulp and Paper in Newfoundland, which currently uses NaOH as a pH buffer in its treatment system. Three separate effluent samples were tested. On average, 12.8 mL of a CKD neutralizing solution (0.4625 g CKD/L distilled water) raised the pH of 100 mL of effluent from pH 6.4—7.0 to pH 8.0. There was no significant problem with excess sedimentation at pH 8.0 and 9.0. However, there was a significant decrease in soluble reactive phosphorus (SRP-P) concentrations, probably through precipitation of calcium phosphate, resulting in a mean loss of 84.7 µg SRP-P per litre of CKD-treated effluent. The use of CKD as a neutralizing agent may be economically feasible, since the costs of adding increased phosphorus as nutrient were less than the costs associated with adding NaOH. Further experimentation needs to be done to determine the efficacy of CKD on a larger scale.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.095
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.000

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 head0.050
GPT teacher head0.332
Teacher spread0.282 · 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.

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

Citations9
Published2000
Admission routes2
Has abstractyes

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