Valorization of rendering industry wastes and co-products for industrial chemicals, materials and energy: review
Bibliographic record
Abstract
Over the past decades, strong global demand for industrial chemicals, raw materials and energy has been driven by rapid industrialization and population growth across the world. In this context, long-term environmental sustainability demands the development of sustainable strategies of resource utilization. The agricultural sector is a major source of underutilized or low-value streams that accompany the production of food and other biomass commodities. Animal agriculture in particular constitutes a substantial portion of the overall agricultural sector, with wastes being generated along the supply chain of slaughtering, handling, catering and rendering. The recent emergence of bovine spongiform encephalopathy (BSE) resulted in the elimination of most of the traditional uses of rendered animal meals such as blood meal, meat and bone meal (MBM) as animal feed with significant economic losses for the entire sector. The focus of this review is on the valorization progress achieved on converting protein feedstock into bio-based plastics, flocculants, surfactants and adhesives. The utilization of other rendering streams such as fat and ash rich biomass for the production of renewable fuels, solvents, drop-in chemicals, minerals and fertilizers is also critically reviewed.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".