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Record W2944878196 · doi:10.1680/jenes.18.00035

Optimisation of conditions of phosphorus release from pharmaceutical waste sludge

2018· article· en· W2944878196 on OpenAlexvenueno aff
Ping Zeng, Juan Li, Yonghui Song, Jian Wei

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsnot available
FundersChinese Research Academy of Environmental Sciences
KeywordsPhosphorusPhosphateChemical oxygen demandOrganic matterPulp and paper industryActivated sludgeChemistryEnvironmental scienceWastewaterEnvironmental engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

With the short supply of phosphorus (P) resources all over the world, more and more phosphorus-rich excess sludge is produced. In this study, a pharmaceutical surplus activated sludge was subjected to alkali and ultrasonic treatment to promote the release of phosphorus. The optimisation of phosphorus release from pharmaceutical surplus activated sludge by alkali and ultrasonic treatment was investigated. The response surface methodology was adopted to optimise the conditions for phosphorus and organic matter release. A quadratic model was established to describe phosphorus and organic matter release. The variance was analysed by using the Design-Expert software, and the optimum operation conditions were decided on. The results indicated that the quadratic model could well fit the relationship between the release of phosphate phosphorus (PO 4 3− -P) and chemical oxygen demand (COD) and the influencing factors. The order of influence on the release of phosphate phosphorus and COD was as follows: pH > total solids > ultrasonic power > time. The maximum release of phosphate phosphorus was 15·3%.

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 score0.179
Threshold uncertainty score0.441

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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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