Understanding the environmental impact of large-scale cellulose nanocrystals production: Case studies in regions dependent on renewable and fossil fuel energy sources
Bibliographic record
Abstract
Favorable functional properties of cellulose nanocrystals (CNCs) in several end-use application areas, as well as its ‘green’ credential as a bio-based material stimulate significant interest in scaling up the manufacture of CNCs. As in any other process, there exist several design options for the overall process and decisions on adoption of a specific plant configuration should be based on economic as well as environmental data, preferably from the life cycle assessment perspective. In this study we establish a benchmark LCA study of a conceptual large-scale CNC manufacturing process based on sulfuric acid hydrolysis. We then use the benchmark process model to explore several plant configuration scenarios and sensitivity of optimal plant configurations to energy mix of different regions. Results of LCA study suggest the optimal plant configuration to include partial recycle of sulfuric acid, which allows to attain the minimum cradle-to-gate environmental impacts. This study provides benchmark figures of LCA impacts of CNC manufacture, which could be used for the assessment of carbon footprint and other environmental metrics of final products manufactured from CNCs.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".