Production Cost of Biocarbon and Biocomposite, and Their Prospects in Sustainable Biobased Industries
Bibliographic record
Abstract
This study evaluated the economic prospects of biocarbon and biocomposite in the automotive industry and bioeconomy. The production cost of biocarbon produced from Miscanthus (a perennial grass), biocarbon-reinforced polypropylene (PP) composite (hereafter referred to as biocomposite), and automotive components are determined. The production cost of biocomposite was compared with inorganic filler-reinforced polymer composite (a conventional composite, i.e., talc reinforced PP composite). The production cost of biocarbon and biocomposite is estimated to be $513.1/ton and between $3536.7–$3647.3/ton, respectively (all dollar figures are in Canadian dollars). On the other hand, the cost of the conventional composite is likely to be $3544.8/ton. However, the production cost of an automotive component can be reduced by 9–11% compared with the conventional component if the components are produced from biocomposite. Further, this study determined the net present values (NPV) of a biocarbon plant and a composite manufacturing plant. The NPV of a biocarbon plant ($42.9 million) and a composite manufacturing plant ($34.0–$34.8 million for biocomposite and $34.7 million for conventional composite) showed that both the biocarbon and composite manufacturing phases are economically attractive. We concluded that by taking an industrial symbiosis approach, the biocomposite industry can be financially more attractive and contribute more to the bioeconomy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".