Recent trends on techno-economic assessment (TEA) of sugarcane biorefineries
Bibliographic record
Abstract
Sustainability challenges, e.g., climate change, resource depletion, and expanding populations, have triggered a swift move towards a circular bio-economy which is expected to evolve progressively in the coming decades. However, the transition from a fossil fuel-based economy to a bio-based economy requires the exploitation of scientific innovations and step changes in the infrastructure of chemical industry. Biorefineries have been extensively investigated for biofuel production from first and second generation feedstocks, whereas some research activities have been conducted on production of biochemical and biopolymers from renewable resources. Techno-economic evaluation of diverse technologies for production of biofuels and biochemical is a crucial step for decision making in the development of bio-economy. This contribution focuses on the economic studies carried out on biorefineries converting sugarcane bagasse, due to its availability and importance in the South African context, into value-added products. Recent studies on biofuel production via biochemical pathway, e.g., ethanol, butanol, or thermochemical pathway, e.g., methanol and bio jet fuel as well as production of biochemicals with high market demands and diverse applications such as lactic acid, succinic acid, and xylitol have been briefly reviewed. In addition, an overview on the production of biopolymers such as polyl-lactic acid and bio-based monomers, i.e., butanediol, from sugarcane bagasse is reported.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".