A critical review of multiple alternative pathways for the production of a high-value bioproduct from sugarcane mill byproducts: the case of adipic acid
Bibliographic record
Abstract
Biobased fuels, chemicals, and materials can replace fossil fuel products and mitigate climate change. Sugarcane mills have the potential to produce a wider range of biobased chemicals in a similar approach to bioethanol production, including adipic acid. Multiple alternative pathways for converting simple sugars into adipic acid have been described, with the potential for integration into a sugar mill. The economics and expected greenhouse gas emissions reductions compared to fossil-based adipic acid were investigated in the present study to identify preferred pathways for implementation in sugarcane biorefineries. Nine biobased pathways for adipic acid production were screened for technical performances, resulting in the selection of four preferred options for rigorous comparison, i.e., direct microbial conversion of sugars, and production via cis,cis-muconic acid, glucaric acid, and glycerol as intermediate, obtained from sugars. The minimum selling prices of adipic acid for an attractive return on investment were determined for these pathways, using either A-molasses or a combination of A-molasses and pretreated sugarcane lignocelluloses in biorefineries designed to be energy-self-sufficient. Adipic acid production from A-molasses via cis,cis-muconic acid was the best overall performing scenario with the lowest minimum selling price of USD 2,538/Mt and lowered greenhouse gas emissions (2,325 g CO2 eq/kg wet) compared to fossil-based adipic acid production. The scenarios with combined A-molasses and lignocellulosic feedstock had increased minimum selling prices by 29 to 101% compared to adipic acid production from A molasses via cis,cis-muconic acid.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".