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Effects of Irradiation Intensity and pH on Nutrients Release and Solids Destruction of Waste Activated Sludge Using the Microwave‐Enhanced Advanced Oxidation Process

2010· article· en· W2249132669 on OpenAlexafffund
W.I. Chan, Ping Liao, K.V. Lo

Bibliographic record

VenueWater Environment Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryActivated sludgeIrradiationNutrientParticle sizePulp and paper industryWaste managementSewage treatmentOrganic chemistry

Abstract

fetched live from OpenAlex

Using the microwave-enhanced advanced oxidation process (MW/H2O2-AOP), the pH and irradiation intensity on waste activated sludge samples were investigated to provide insight to the athermal effects on nutrients release, solids destruction, particle size distribution and dewaterability, and to demonstrate their interrelationships. Carbonaceous matters and nutrients released into solution depended on the irradiation intensity and time. Higher irradiation levels tended to be more effective in the solubilization of nutrients and had more pronounced effects in the dewaterability of sludge. In terms of particle size distribution, detectable particles increased in size for treatments in acidic conditions, while the dewaterability of treated sludge was improved. In treatments under neutral and alkaline conditions, the particle size range increased, with more small particles formed, thereby significantly deteriorating the dewaterability of sludge treated in alkaline conditions. The best results for the solubilization of nutrients were in alkaline conditions with high irradiation power, but dewaterability of the sludge was compromised. Sludge treatment with the MW/H2O2-AOP in acidic conditions with high irradiation power yielded the best dewaterable sludge and significant nutrient solubilization; therefore, it is the recommended treatment condition for activated sludge.

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 score0.055
Threshold uncertainty score0.297

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 head0.012
GPT teacher head0.260
Teacher spread0.248 · 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

Citations26
Published2010
Admission routes2
Has abstractyes

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