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Enhancement of Biogas Production in Two-Phase Anaerobic Fermentation System for Lower-Temperature Applications

2014· article· en· W2142344979 on OpenAlexvenueno aff
Hongqiong Zhang, Wenzhe Li, Yong Sun, Xiangyou Wang

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2014
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsnot available
FundersDepartment of Education, Heilongjiang ProvinceDepartment of Bioproducts and Biosystems Engineering, University of MinnesotaMinistry of Education of the People's Republic of ChinaUniversity of Minnesota
KeywordsBiogas productionBiogasFermentationAnaerobic exerciseProduction (economics)Pulp and paper industryEnvironmental scienceProcess engineeringAnaerobic digestionWaste managementFood scienceChemistryBiologyEngineeringMethaneEcologyEconomics

Abstract

fetched live from OpenAlex

For the purpose of enhancing the biogas production and operation stability of system at low temperature, a two-phase anaerobic fermentation facility for rural household energy generation was proposed. In this facility, the quantity of the fermented materials could be balanced by controlling the hydraulic retention time, which based on pH of the acidification tank and the fermentation tank. In addition, a portion of the biogas generated could be used to heat the acidification tank and the fermentation tank. Results shows that the optimal daily production rate of biogas for 1 m3 fermentation liquid was about 1.47 L/L d-1 at a mixing ratio of cow: swine: chicken manure was 3: 1: 0.5. The production rate obtained in this study was more than four times higher than that from traditional single-phase processes (0.35 L/L d-1). About 5.43 m3 biogas can be produced daily per household with an average CH4 content of 76.8%. The two-phase process developed in this work will also reduce environmental pollution and increase energy production efficiencies.

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 score0.293
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
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 head0.258
Teacher spread0.251 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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