Decarbonising bioenergy through biomass utilisation in chemical looping combustion and gasification: a review
Bibliographic record
Abstract
Abstract Biomass valorisation for bioenergy is crucial for establishing a sustainable low-carbon circular bioeconomy and addressing societal and environmental challenges. As global demand for renewable energy grows, effective waste management using biomass becomes increasingly vital. However, successful implementation requires addressing specific local, scientific, and conceptual challenges, including technological intricacies, resource availability, scalability, and environmental impacts. Advancements in chemical looping gasification and chemical looping combustion are reviewed here as essential components of bioenergy with carbon capture and storage technologies. Chemical looping technologies have substantially contributed to decarbonising various industries, supported by precise trends. The review highlights the potential of chemical looping combustion and gasification in biomass utilisation for generating bioenergy and mitigating carbon emissions. Moreover, the advantages of chemical looping processes enhance their feasibility and applicability across different contexts including (1) significant increase in biomass conversion efficiency through chemical looping gasification, leading to substantial reductions in greenhouse gas emissions, (2) progress in chemical looping combustion, enhancing carbon capture and storage capabilities and contributing to a closed carbon cycle, (3) detailed insights into integrating chemical looping technologies into industrial sectors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".