Deciphering acidogenic process towards biohydrogen, biohythane, and short chain fatty acids production: multi-output optimization strategy
Bibliographic record
Abstract
Optimization of process parameters is crucial to understand the acidogenic fermentation process and its regulation towards the production of specific metabolites, viz., biohydrogen (H2), methane (CH4), biohythane (H2+CH4), and volatile fatty acids (VFA). Design of experiments (DOE) based on orthogonal array (OA) was employed to optimize and evaluate the influence of eight critical factors on multiple metabolic output parameters. Analysis of the experimental data revealed a specific influential regime of selected factors in terms of biogas generation and/or VFA synthesis. Application of pretreated inoculum as biocatalyst and high substrate concentration showed substantial enhancement of both H2 and VFA production. High COD of 10 g/L in combination with pretreated inoculum resulted in higher cumulative hydrogen production (CHP), while the higher fraction of acetic acid in the fermentation broth resulted in a higher degree of acidification (DOA). H2/H2+CH4 ratio varied from 0.1 to 0.97 and the application of untreated inoculum was shown to favor biohythane (H2+CH4) production. Overall, this communication holistically documented the feasibility of regulating acidogenic fermentation process towards a spectrum of metabolic end products of high value, while waste treatment was also achieved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".