Catalytic Valorization of Organic Solid Waste: A Pilot-Scale Run of Sugarcane Bagasse
Bibliographic record
Abstract
Organic solid waste treatment is crucial for enhancing environmental sustainability, promoting economic growth, and improving public health. Following our previous organic solid waste upgrading technique, a further two-step pilot-scale run, using sugarcane bagasse as the feedstock, has been successfully conducted with long-term stability. Firstly, the sugarcane bagasse was treated under mild conditions (400 °C and 1 bar of CH4), and this catalytic Methanolysis treatment resulted in a bio-oil with a yield of 60.5 wt.%. Following that, it was subjected to a catalytic Methano-Refining process (400 °C and 50 bar of CH4) to achieve high-quality renewable fuel with a liquid yield of 95.0 wt.%. Additionally, this renewable fuel can be regarded as an ideal diesel component with a high cetane number, high heating values, a low freezing point, low density and viscosity, and low oxygen, nitrogen, and sulfur contents. The successful pilot-scale catalytic upgrading of sugarcane bagasse further verified the effectiveness of this methane-assisted organic solid waste upgrading technique and confirmed the high flexibility of this innovative technology for processing a wide spectrum of agricultural and forestry residues. This study will shed light on the further valorization of organic solid waste and other carbonaceous materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".