Technoeconomic assessment of different biorefinery approaches for a spent sulfite liquor
Bibliographic record
Abstract
Abstract BACKGROUND Spent sulfite liquor, a by‐product obtained in the process of manufacturing dissolving pulp, contains 26% of sugars that can be valorized in order to obtain high value‐added products by means of biorefinery processes. A technoeconomic assessment of three options, furfural, xylitol and ethanol, has been developed with the purpose of identifying which alternative is the best for the case study mill. RESULTS Different techniques of fractionation/detoxification of the spent liquor such as ultrafiltration, resins or adsorption were tested; anionic resins were selected as the most feasible. A technical evaluation of the three biorefinery options producing 19.92, 15.84 or 14.64 t day−1 of furfural, xylitol and ethanol, respectively, was performed. The study was pursued with the sizing and costing of the equipment. For the economic evaluation, the fixed capital invested and the manufacturing costs for each valorization option were computed as well as the return period and the net present value. In addition, a sensitivity analysis was performed for the most promising option. CONCLUSIONS Spent sulfite liquor can be profitably processed in the considered mill. According to the data obtained by simulation and the analysis performed, the best valorization option is the production of xylitol. © 2015 Society of Chemical Industry
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".