LignoForce System for the Recovery of Lignin from Black Liquor: Feedstock Options, Odor Profile, and Product Characterization
Why this work is in the frame
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Bibliographic record
Abstract
Lignin can be precipitated from kraft black liquor (BL) through the addition of an acidifying agent such as carbon dioxide or sulfuric acid. In most of the existing lignin precipitation processes that are using acid addition, sufficient acid is added to drop the pH of the black liquor from about 13–14 to about 9–10, followed by lignin particle coagulation, lignin slurry filtration, and lignin cake washing with sulfuric acid and water. At pH values of less than 11, the potential exists for the generation of significant quantities of totally reduced sulfur (TRS) compounds and other volatile sulfur species. Such compounds which include hydrogen sulfide, methyl mercaptan, dimethyl sulfide, and dimethyl disulfide are strongly odorous compounds with well-known negative effects on human health and other forms of life. To address this problem, as well as other problems associated with existing lignin recovery processes, FPInnovations and Noram recently developed a new process called the LignoForce System. This process employs a black liquor oxidation step to convert TRS compounds present in kraft black liquor to nonvolatile species. This paper discusses the applicability of the LignoForce System to several feedstock black liquors (e.g., softwood, hardwood, and eucalyptus) as well as the sulfur compound outgassing potential from various stages of this process compared to a reference case in which the black liquor was not oxidized. In addition, the emission of volatile sulfur and organic compounds from the two lignin products at different temperatures is discussed and compared.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it