Techno‐economic and market analysis of two emerging forest biorefining technologies
Bibliographic record
Abstract
Abstract A new forest biorefining model is emerging in which high‐value materials would be produced alongside a cellulosic sugar by‐product in facilities scaled to match fiber availability in northern forests. As these emerging biorefining technologies are being developed by private entities, the economics associated with them are little known. The goal of this study is to use publicly available information to carry out an initial techno‐economic assessment for two emerging biorefining technologies: one that produces sugar and hydrolysis lignin (H‐lignin) from thermomechanical pulping (TMP) and another that produces sugar, lignosulfonate, and nanocellulose. The break‐even price for H‐lignin and nanocellulose is estimated for the respective technology and a credit is applied for cellulosic sugar and other co‐product sales based on current market value. It was found that the minimum product selling price (MPSP) for H‐lignin was within the range of high purity lignin but not enough is known about the properties of H‐lignin to determine if this is a reasonable value for prospective end uses. The estimated MPSP of nanocellulose was found to be considerably lower than for more conventional nanocellulose‐producing methods that use Kraft or dissolving pulp as a starting point. The nanocellulose produced through the second process modeled has different properties than conventional nanocellulose, which need to be further explored. Having a sense of the cost to produce these novel materials will help to direct research on viable end uses. The methodology presented, using only publicly available information, can also be replicated for other emerging technologies. © 2021 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".