Evaluation and modelling of methane production from corn stover pretreated with various physicochemical techniques
Bibliographic record
Abstract
Lignocellulosic by-products from agricultural crops represent an important raw material for anaerobic digestion and clean renewable, which is a key component of the circular economy. Lignocellulose is recalcitrant to biodegradation and pretreatments are required to increase methane yield during anaerobic digestion. In this work, the efficacy of different physicochemical pretreatments was compared using corn stover biomass as substrate. Anaerobic digestion of untreated and pretreated corn stover was performed in batch mode at mesophilic temperature (38°C) and organic matter solubilization of pretreated substrates was also investigated. The highest organic matter solubilization occurred in autoclave pretreatment (soluble chemical oxygen demand = 5630 ± 42 mg O 2 L −1 ). However, the highest methane yield was obtained using alkaline pretreatment (367 ± 35 mL CH 4 g −1 VS added ). Alkaline pretreatment increased methane yield by 43.3% compared to untreated control (256 ± 15 mL CH 4 g −1 VS added ). Two mathematical models (i.e. first-order kinetics and transfer function) were utilized to fit the experimental data with the aim of assessing anaerobic biodegradation and to obtain the kinetic constants in all cases studied. Both models adequately fit the experimental results. The kinetic constant, k, of the first-order model increased by 92.8% when stover was pretreated with sulphuric acid compared with control. The transfer function model revealed that the maximum methane production rate, R m , was obtained for the sulphuric acid treatment, which was 63.5% higher compared to control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".