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Record W2072611481 · doi:10.1002/jctb.1965

Sewage sludge treatment using microwave‐enhanced advanced oxidation processes with and without ferrous sulfate addition

2008· article· en· W2072611481 on OpenAlexaff
K.V. Lo, Ping Liao, Gui Q. Yin

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSewage sludgeFerrousChemistryAmmoniaSulfateChemical oxygen demandNuclear chemistrySewageIron sulfateSewage treatmentWaste managementOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract BACKGROUND: Microwave‐enhanced advanced oxidation processes with and without the addition of ferrous sulfate (MW/H 2 O 2 /Fe 2+ ‐AOP and MW/H 2 O 2 ‐AOP respectively) were studied for reduction of solids and solubilisation of nutrients from secondary sewage sludge. RESULTS: For the MW/H 2 O 2 /Fe 2+ ‐AOP the yields of solubilisation of orthophosphate and ammonia decreased with increasing temperature. The best results (88.1 mg L −1 for orthophosphate and 22.7 mg L −1 for ammonia) were obtained at a treatment temperature of 40 °C. In contrast, the MW/H 2 O 2 ‐AOP had an advantage when it was operated at higher temperatures of 60 and 80 °C. The highest yields of solubilisation were obtained at 60 °C for orthophosphate (81.1 mg L −1 ) and at 80 °C for both ammonia (35.0 mg L −1 ) and soluble chemical oxygen demand (1954 mg L −1 ). Over the temperature range used in this study, the MW/H 2 O 2 ‐AOP gave a better performance than the MW/H 2 O 2 /Fe 2+ ‐AOP. CONCLUSION: For sewage sludge treatment the MW/H 2 O 2 ‐AOP is more effective than the MW/H 2 O 2 /Fe 2+ ‐AOP in terms of solid reduction and nutrient solubilisation. It will also be more cost‐effective, as it does not require iron addition in the process. Copyright © 2008 Society of Chemical Industry

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 score0.071
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.237
Teacher spread0.225 · 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

Citations19
Published2008
Admission routes1
Has abstractyes

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