Techno-economic and environmental assessment of a sugarcane biorefinery: direct and indirect production pathways of biobased adipic acid
Bibliographic record
Abstract
Adipic acid (ADA) is a highly valuable industrial dicarboxylic acid used largely as a precursor of nylon 6,6 production. It is currently synthesized via a petrochemical process that accounts for over 80% of the global industrial N2O emissions. Biobased ADA offers a cleaner alternative but requires technological advancements in microbe and bioprocess performance to be commercially relevant. An in-depth feasibility analysis was conducted to evaluate two biobased pathways for the production of ADA, modeled as integrated sugarcane biorefineries in Aspen Plus®. The pathways examined were: (1) direct fermentation of sugars to ADA (S1-ADA) and (2) hydrogenation of biobased cis,cis-muconic acid to ADA (S2-ccMA-ADA). The impact of improvements to key bioprocess metrics (product yield, titer, and volumetric productivity) on the minimum selling price and greenhouse gas (GHG) emissions for both pathways was also evaluated in a full-factorial study. S1-ADA demonstrated the highest feasibility potential, achieving minimum selling prices and GHG emissions that were 33.3% and 78.7% lower, respectively, than those of fossil-based production. These results were obtained under conditions of optimal strain performance and bioprocess efficiencies. However, under comparable technological advancements, the best-achievable results for S2-ccMA-ADA indicated a green premium of 13.4% alongside a 68.4% reduction in emissions compared to the fossil-based product. Consequently, the direct biobased pathway (S1-ADA) shows greater potential to compete with and eventually replace its fossil-based counterpart once optimized. This finding highlights the need to prioritize S1-ADA for further biotechnological development.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".