Enhancing waste degradation and biogas production by pre-digestion with a hyperthermophilic anaerobic bacterium
Bibliographic record
Abstract
The hyperthermophilic anaerobic bacterium, Caldicellulosiruptor bescii, is effective in degrading and solubilizing lignocellulosic materials. Laboratory studies have characterized the chemistry of the process for crystalline cellulose and switchgrass, but the data are insufficient for engineering commercial plants to use C. bescii for pre-digestion of waste streams. The purpose of this study is three-fold: 1) to identify any potential toxicities in C. bescii pre-digestion and biogas production from several wastes; 2) to determine the potential enhancement of biogas production by anaerobic digestion of pre-digested dairy manure and waste activated sludge; and 3) to identify variables that must be quantified and controlled for engineering commercial, continuous-flow systems for waste disposal and biogas production incorporating C. bescii pre-digestion. Tests were run at lab-, bench- and pilot plant-scale with C.bescii pre-digestion and controls run at 75°C and pH 7-8 followed by mesophilic anaerobic digestion at 37-41°C. The lab- and bench-scale tests demonstrate that C. bescii is capable of growing on several organic wastes and pre-digestion with C. bescii increases conversion of waste into biogas, typically by a factor of 2 or more. Incorporation of C. bescii pre-digestion in an optimized commercial system is predicted to provide 75-85% volatile solids conversion to biogas with 75% methane when digesting dairy manure and sewage sludge. Achieving these results at a commercial scale requires further work to quantify C. bescii growth and enzyme production rates, as well as rates of base- and enzyme-catalyzed hydrolysis of the polymeric materials, e.g., lignocellulose, in the waste in order to optimize retention times.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".