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Record W2170409804 · doi:10.5539/jsd.v6n2p26

Application of a Series of Continuously Fed Aerated Tank Reactors System for Recycling of Swine Slurry Nutrients

2013· article· en· W2170409804 on OpenAlexvenueno aff
Anni Alitalo, Tuomas Pelto-Huikko, Erkki Aura

Bibliographic record

VenueJournal of Sustainable Development · 2013
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsnot available
FundersAcademy of Finland
KeywordsAerationOdorSlurryManurePhosphorusNutrientPulp and paper industryEnvironmental scienceNitrogenWaste managementWastewaterSewage treatmentChemistryEnvironmental engineeringAgronomyBiology

Abstract

fetched live from OpenAlex

Single tank aeration systems operated in batch mode or sequential batch reactors based on intermittent aeration are generally applied for swine manure treatment in order to reduce nutrient content and odor. This study evaluated the feasibility of aerobic biological treatment conducted in a serial arrangement of continuously fed aerated tank reactors configuration with swine slurry on a pilot-scale. Compared to municipal wastewater treatment swine slurry rich in nutrients and solids requires different treatment standards for the applied methods to be economically and ecologically relevant. This study presents a sequential process scheme, in which biological treatment serves as a means of achieving a pH raise. This makes it possible to separate part of the nitrogen by stripping and enhances slurry precipitating/coagulation characteristics without substantial carbon or nitrogen loss in gaseous form (which is the opposite to that in a conventional active sludge process). The system was run with a hydraulic retention time (HRT) of three to four days (whole system), and a feedback of 0.8±0.2. During treatment, total organic carbon (TOC) reduction varied between 2.4% and 14.6% and the total nitrogen concentration change was less than 3%. Nearly 50% of the total phosphorus was reduced. The results indicated that the odor of liquid manure had decreased efficiently at best to a level at which there was no odor or only very faint odor. Concurrently, manure pH rose to 8.7.

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)0.001
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 score0.018
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.214
Teacher spread0.207 · 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
Published2013
Admission routes1
Has abstractyes

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