Biogas Upgrading Using Ash from Combustion of Wood Fuels: Laboratory Experiments
Bibliographic record
Abstract
The value of biogas produced at small scale facilities, such as farm scale biogas plants, can increase by upgrading it to vehicle fuel quality. However, commercial upgrading technologies available today are very costly for small scale applications. Ash from combustion of wood fuels has a high content of Ca, which indicates favourable conditions for a high CO2 uptake capacity from biogas. The objective of this study was to assess the CO2 uptake capacity of ash from combusted wood pellets and wood chips in a laboratory scale solid bed reactor using an inlet gas mixture of CO2 and CH4 with the aim to reach > 97 % CH4 in the outlet gas. A gas with a defined composition of 65 % CH4 and 35 % CO2 was passed through a moisturised solid ash bed in an up-flow manner. The gas quality in the outlet gas and the CO2 uptake capacity of the ash was assessed. Bottom ash from combusted wood pellets showed the best uptake capacity of 0.20 g CO2/g dry ash, which is 4-8 higher than studies where municipal solid waste incineration bottom ash was tested. The outlet gas from the ash reactor contained high concentrations of methane (up to 99.6 %) and the gas contained no CO2 until CO2 breakthrough occurred in the ash bed. Furthermore, the pH of the ash was reduced by 2 to 3 units due to the carbonation, which improves the prerequisites for recycling the ash to forestry. It was concluded that an ash bed with Ca rich wood ash has the ability to reach vehicle fuel quality regarding CH4 concentration. Based on the results, a biogas plant of 1 GWh (3.6 TJ) per year would require approx. 650 tonnes of dry wood ash a year with an uptake of 0.20 g CO2/g dry ash and an inlet biogas composition of 60 % CH4 and 40 % CO2.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".