Valorising Agricultural Residues into Pellets in a Sustainable Circular Bioeconomy
Bibliographic record
Abstract
It is a truism by now that the combustion of fossil fuels has execaerbated climate change, and its repercussions. Biomass in pelletised form, will emerge as substitutes, in the circular bioeconomies of the future. This brief review focuses on the utilisation of agricultural residues as raw materials for pellets, and explores the aspects of sustainability – socio-cultural, economic, environmental, and techno-functional – in the 20-plus peer-reviewed articles selected for that purpose using Scopus with a set of search-phrases. The articles are case studies dated between 2012 to 2023, tracing their provenance to different countries in the world – Brazil, Canada, China, Denmark, Greece, India, Italy, Mexico, Peru, Spain, Thailand, Türkiye, Zambia, etc. Among the many gleanings which are reported in this review, some deserve mention here in the abstract. The social aspect of sustainability has not been studied as much as the economic and environmental. The case studies emphasize the importance of adapting the pelleting process to the properties of the agricultural/horticultural residues and the prevalent local conditions. It is encouraging to note that there is a surfeit of agricultural residues (corn, coffee, quinoa, beans, oats, wheat, olives, tomatoes, pomegranates, grapes, etc. in the articles reviewed) which can be valorised to pellets, also in combination with the in-vogue forestry wastes. This is more advisable if the status quo is open burning of such residues in the fields. The journey towards the sustainable development goals (SDGs) will be aided by investments in such biorefinery-projects, SDG 17 is extremely vital for their success – collaboration and cooperation among several stakeholders around the world. This review, though based on only 20-plus articles from around the world, is an in-depth analysis which promises to be of interest to decision-makers and sustainability-specialists keen on contributing to the transition to a circular bioeconomy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".