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Record W2906103211 · doi:10.3168/jds.2018-14966

Production of recycled manure solids for bedding in Canadian dairy farms: I. Solid–liquid separation

2018· article· en· W2906103211 on OpenAlexaffabout
Sébastien Fournel, Stéphane Godbout, P. Ruel, A. Fortin, Mylène Généreux, Caroline Côté, Christine Landry, D. Pellerin

Bibliographic record

VenueJournal of Dairy Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsCentre de Recherche en Sciences Animales de DeschambaultInstitut de Recherche et de Développement en AgroenvironnementUniversité Laval
Fundersnot available
KeywordsSlurryManureSeparator (oil production)Screw pressCentrifugeDry matterTotal dissolved solidsEnvironmental scienceLiquid manurePulp and paper industryEffluentSuspended solidsWaste managementAnimal scienceMaterials scienceAgronomyEnvironmental engineeringEngineeringMetallurgyWastewater

Abstract

fetched live from OpenAlex

Canadian dairy producers have an increasing interest in recycled manure solids (RMS) as bedding material because of reduced availability of traditional bedding resources. Information regarding methods to obtain RMS and composition of RMS is very limited. Hence, a 2-part investigation was developed to compare the performances of 3 mechanical solid-liquid manure separators (part I) and 4 composting methods (part II; companion paper in this issue) for the production of high quality RMS. In this first study, a roller press, a screw press, and a decanter centrifuge were tested for the separation of slurry manure from a commercial dairy farm. During the experiment, the quantity of slurry manure processed and the volume and mass of the liquid and solid fractions were measured. The energy consumption of each separator was recorded, and samples of the slurry, liquid, and solid effluents were collected for analysis. The type of separator did not significantly influence the chemical and bacteriological composition of RMS produced. The choice of a separator for Canadian dairy producers should thus be based on the equipment cost and its capacity, targeted solids dry matter (DM) content and structure, and fertilizing quality of the separated liquid. The decanter centrifuge produced the solid phase with the highest DM and best separation efficiencies for DM, N, and P. However, its low production capacity (1.5 m 3 /h vs. 9.1–20.3 m 3 /h) combined with its high acquisition cost (Can$145,000 vs. Can$75,000) and energy consumption (4.99 kWh/m 3 vs. 0.10–0.35 kWh/m 3 ) reduce its technical and profitability values. Besides, the centrifuge produced fine structured RMS and a low-quality liquid fraction, not suitable as dairy cow bedding and fertilizer, respectively. Both presses reached acceptable production capacity at a minimal operation cost. However, the poor performance in terms of DM (25%) of the model of screw press used in this study produced RMS unsuitable for immediate use without further processing. The model of roller press used in this study had the advantages of almost reaching the recommended DM content in RMS (>34%), being flexible in terms of inputs, and producing fluffy RMS. Nevertheless, its compression process seemed to allow greater passage of solids into the liquid fraction compared with the screw press. Part II of this work explores different composting methods to reduce the health risks associated with screw-pressed RMS before their use as bedding.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.858

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.031
GPT teacher head0.320
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations39
Published2018
Admission routes2
Has abstractyes

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