Technoeconomic and Life-Cycle Assessment for Electrocatalytic Production of Furandicarboxylic Acid
Bibliographic record
Abstract
2,5-Furandicarboxylic acid (FDCA) is a platform chemical for polyethylene furanoate (PEF) manufacturing, a promising biobased and green alternative to polyethylene terephthalate (PET) with a market size of 1.8 million tonne/annum. There are several routes to produce FDCA, all through 5-hydroxymethylfurfural (HMF) conversion. The traditional thermochemical process is highly energy intensive with a low yield. The electrocatalytic pathway, on the other hand, is gaining increased interest for it makes the process control more efficient, achieves a higher yield, and more importantly can be driven by renewable electricity to lower the environmental impact compared to the thermochemical process. This study assesses the economic aspects and environmental impacts of the electrochemical production of FDCA. It is found that the net present value (NPV) of the integrated electrochemical conversion and product separation plant is highly profitable, $72 million for 100 tonne/day production of FDCA, under optimistic conditions. It also reveals that the HMF price has significant impact on process economics, and the current density has the largest scope of improvement. The life-cycle assessment (LCA) results indicate that processes related to HMF production contribute the most to the overall environmental impacts─calling for low impact HMF production processes, with cost reductions─however, the impacts of the electrochemical route are much lower in comparison with the thermochemical route.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".