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Record W4378905607 · doi:10.18331/brj2023.10.2.2

Thermodynamic method for analyzing and optimizing pretreatment/anaerobic digestion systems

2023· article· en· W4378905607 on OpenAlexvenueno aff
Lee D. Hansen

Bibliographic record

VenueBiofuel Research Journal · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsnot available
Fundersnot available
KeywordsMethanogenesisAnaerobic digestionMethanosaetaChemistryBiogasPropionateMethanosarcinaValerateBicarbonateMethanogenMethaneBiochemistryBiologyButyrateEcologyFermentationOrganic chemistry

Abstract

fetched live from OpenAlex

This paper builds a quantitative thermodynamic model for the microbial hydrolysis process (MHP, which uses Caldicellulosiruptor bescii at 75°C for pre-digestion) for producing biogas from a 5-10% aqueous suspension of dairy manure (naturally buffered near pH 7.8 by ammonium bicarbonate) by anaerobic digestion with a mix of acetoclastic and syntrophic methanogenesis. Standard Gibbs energy changes were calculated for the major reactions in pre-digestion, for reactions producing H2, acetate, and CO2 in the digester, and for methanogenesis reactions in the digester. The available data limit the study to analyzing reactions in the digester to reactions of short-chain volatile fatty acids anions. Results are presented as curves of ΔrxnG (Gibbs energy change) vs. acetate concentration. The H2(aq) concentration must be above 1.2×10-9 M to get significant syntrophic methanogenesis, i.e., for ΔrxnG to be negative. The results show syntrophic methanogenesis of propionate, butyrate, and valerate slows as acetate concentration increases because hydrogen production also decreases, and consequently, biogas production from syntrophic methanogenesis slows as acetate increases. Bicarbonate also inhibits both acetoclastic and syntrophic methanogenesis but is necessary to prevent acidification (souring) of the digester. At identical steady-state conditions, acetoclastic methanogenesis runs about 1.4 times faster than syntrophic methanogenesis. Because syntrophic methanogenesis produces acetate catabolized by acetoclastic methanogens, both types of methanogens are necessary to maximize biogas production. The culture in the digester is predicted to evolve to optimize the ratio of acetoclastic methanogens to syntrophic methanogens, a condition signaled by a constant, low acetate concentration in the digester effluent. Obtaining volatile solids reduction as high as 75% with MHP requires a feedstock with less than 25% lignin and a culture of acetoclastic methanogens and syntrophic methanogens and their symbiotic bacteria.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.041
GPT teacher head0.364
Teacher spread0.323 · 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 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

Citations24
Published2023
Admission routes1
Has abstractyes

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