Integrated and closed‐loop biorefinery strategies for efficient waste valorization and biofuel production
Bibliographic record
Abstract
Abstract Advancements in biofuel production technologies are essential for reducing global dependence on fossil fuels and addressing their overexploitation. Many valuable components of biomass, such as cellulose, hemicellulose, and lignin, remain underused in traditional biorefineries, which typically rely on a single feedstock to produce a primary biofuel. In contrast, integrated biorefineries utilize multiple feedstocks and various biomass conversion technologies, resulting in the production of numerous value‐added products and a significant waste reduction. This article reviews emerging biorefinery technologies, including fermentation, anaerobic digestion, densification, torrefaction, pyrolysis, liquefaction, and gasification. These technologies convert waste biomass into a variety of biofuels, such as bioethanol, biobutanol, biohydrogen, biogas, briquettes, biochar, bio‐oil, bio‐crude oil, and syngas. The review emphasizes the integration of biorefinery technologies, focusing on energy‐driven systems and closed‐loop waste utilization and management pathways. Additionally, the article discusses physical, chemical, and biological pretreatment techniques, along with the principles and unit operations associated with both biological and thermochemical biorefinery technologies. It also examines the logistics and supply chain necessary for biorefineries to effectively use diverse biomass sources and expand their biofuel production capabilities. Finally, the article concludes by addressing the need for integrated waste‐to‐energy conversion technologies that ensure process efficiency, byproduct utilization, maximum resource recovery, infrastructure compatibility, reduced carbon footprints, and circular economy strategies.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".