Hydrothermal Liquefaction of Pulp and Paper Mill Residues for Biocrude Production
Bibliographic record
Abstract
The pulp and paper industry is one of the leading waste-generating industries globally. With the rich energy content of these wastes and many of these mills not paying attention/implementing efficient waste disposal methods, it has become imperative that research efforts on pulp and paper waste valorization be performed; hence this paper. Hydrothermal liquefaction (HTL) technology was adopted due to its ability to transform wet biomass into biocrude. The research studied the effects of reaction parameters such as temperature, residence time, feed concentration, and catalysts on the yield of biocrude. While central composite design (CCD) was used in the design of the experiments, response surface methodology (RSM) was utilized for their optimization. The optimum parametric conditions obtained were the following: temperature: 340 °C; residence time: 56min; and feed concentration: 5%. Zeolite (HZSM-5), gamma-alumina (γ-Al2O3), and activated carbon were utilized as catalysts, and their performances with respect to biocrude yield improvement were evaluated. The order of catalytic effect on the biocrude yield was γ-Al2O3 (25.65%) > HZSM-5 (23.18%) > activated carbon (21.94%). Catalyst characterization was performed on the fresh and spent catalysts to study their properties and to make informed inferences on their impacts on biocrude yield. Based on the findings from this research, necessary conclusions and recommendations for future work are presented.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 | 0.000 |
| 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) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".