Production of 2,5‐furandicarboxylic acid (FDCA) from starch, glucose, or high‐fructose corn syrup: techno‐economic analysis
Bibliographic record
Abstract
Abstract This paper presents a techno‐economic analysis of a low‐cost and high‐efficiency technology for the production of 2,5‐furandicarboxylic acid (FDCA) from starch, glucose, or high‐fructose corn syrup (HFCS). With the design presented here, it is viable to convert starch to glucose through enzymatic hydrolysis followed by catalytic dehydration of glucose with niobium phosphate, an innovative low‐cost catalyst, to produce 5‐hydroxymethyl furfural (HMF). The HMF produced is then converted into FDCA via air oxidation over a cobalt‐manganese mixed oxide catalyst. Three variations of this design are assessed: Scenario 1 starts with starch, and Scenarios 2 and 3 start directly with glucose or HFCS, without the initial starch hydrolysis step. The minimum selling price (MSP) and discounted payback period (PBP) were calculated to investigate the feasibility of the scenarios. A sensitivity analysis was also performed for the key cost drivers, selling price of FDCA, and spent catalysts. All scenarios were feasible; however, the HFCS to FDCA process was the most profitable (MSP 1802 US$/t and a PBP below 5 years). Above all, the feasibility of this technology is mostly affected by variations in recovered catalysts and FDCA selling prices. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".