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Record W4383093146 · doi:10.1016/j.rcradv.2023.200168

Two-stage (liquid-solid) anaerobic mono-digestion of ammonia-rich chicken manure at low-temperature: Operational strategy and techno-economic assessment

2023· article· en· W4383093146 on OpenAlexafffund
Prativa Mahato, Suman Kumar Adhikary, Bernard Goyette, Rajinikanth Rajagopal

Bibliographic record

VenueResources Conservation & Recycling Advances · 2023
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsKjeldahl methodAnaerobic digestionAmmoniaChemistryManureBiogasTotal dissolved solidsPulp and paper industryMethaneDigestion (alchemy)Chicken manureAnimal scienceNitrogenAgronomyChromatographyWaste managementEnvironmental scienceEnvironmental engineeringBiochemistryBiology

Abstract

fetched live from OpenAlex

This study investigated mono-digestion of ammonia-rich chicken manure (CM) in a high-solids anaerobic-digester (HSAD) at low-temperature (20±1°C) adopting the recirculation-percolation mode of operation using liquid-inoculum. HSAD was operated for about 282 days in batch-runs under different organic loading rates (4.3-8.7 gVS/L.d), total solids (65-70%) and total kjeldahl nitrogen (TKN) (23.3- 32.8 g/L) concentrations. It was noted that acclimation of the liquid inoculum to a high concentration of TKN (6.9 g/L) facilitated CM digestion, thereby reducing the start-up phase. The maximum cumulative biogas production rate and specific methane yield were found to be 20 L/d and 0.80±0.12 LCH4g/VSfed, respectively with a methane content over 60% without any signs of inhibition. Operating HSAD at low temperature prevented the accumulation of free-ammonia, which is toxic to methanogens. A techno-economic analysis was also conducted as part of this study to determine the profitability of the system and a 9-year tentative payback period was estimated.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.266
Teacher spread0.255 · 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.

Study designSimulation or modeling
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

Citations6
Published2023
Admission routes2
Has abstractyes

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