Effects of Process Parameters on Hydrolytic Treatment of Black Liquor for the Production of Low-Molecular-Weight Depolymerized Kraft Lignin
Bibliographic record
Abstract
The present research work aimed at hydrolytic treatment of kraft black liquor (KBL) at 200–300 °C for the production of low-molecular-weight depolymerized kraft lignin (DKL). Various process conditions such as reaction temperature, reaction time, initial kraft lignin (KL) substrate concentration, presence of a catalyst (NaOH), capping agent (phenol) or co-solvent (methanol) were evaluated. The research demonstrated effective depolymerization of KL in KBL at 250–300 °C with NaOH as a catalyst at a NaOH/lignin ratio of about 0.3 (w/w) using diluted KBL (with 9 wt. % KL). Treatment of the diluted KBL at 250 °C for 2 h with 5% addition of methanol co-solvent produced DKL with a weight-average molecular weight (Mw) of 2340 Da, at approx. 45 wt. % yield, and a solid residue at a yield of ≤1 wt. %. A longer reaction time favored the process by reducing the Mw of the DKL products. Adding a capping agent (phenol) helped reduce repolymerization/condensation reactions thereby reducing the Mw of the DKL products, enhancing DKL yield and increasing the hydroxyl group content of the lignin. For the treatment of diluted KBL (with 9 wt. % KL) at 250 °C for 2 h, with 5% addition of methanol co-solvent in the presence of NaOH/lignin ≈ 0.3 (w/w), followed by acidification to recover the DKL, the overall mass balances for C, Na and S were measured to be approx. 74%, 90% and 77%, respectively. These results represent an important step towards developing a cost-effective approach for valorization of KBL for chemicals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".