Bio‐based polymers production in a kraft lignin biorefinery: techno‐economic assessment
Bibliographic record
Abstract
Abstract This paper presents a techno‐economic and risk analysis of a kraft lignin (KL) biorefinery (3000 tonne of KL·year‐1 capacity), where KL is depolymerized to produce depolymerized kraft lignin (DKL) as a bio‐substitute to polyol and phenol for the production of bio‐based polymers (polyurethane and phenolic resins). Three scenarios were examined: (i) DKL as a phenol substitute, (ii) DKL as a polyol substitute, and (iii) oxypropylated depolymerized kraft lignin (Oxy‐DKL) a polyol substitute. The Net Present Value was calculated to compare these scenarios. To address the uncertainty risks in feedstock and product price, a sensitivity analysis and a Monte Carlo simulation were performed. Results show that DKL and Oxy‐DKL derived from the KL biorefinery are a feasible bio‐substitute for petroleum‐based polyols with a minimum selling price of 1440 and 1623 US$·t‐1, respectively. However, DKL is likely not feasible when replacing phenol (minimum selling price of 1421 US$·t‐1) due to the current low market price of phenol. The feasibility of the KL biorefinery is highly sensitive to the market prices of the products. Feedstock supply and market demand for lignin‐derived biopolyols are still uncertain; therefore, a supply chain design model is necessary for decision‐making. © 2017 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".