Hydrothermal Carbonization of Green Harvesting Residues (GHRs) from Sugar Cane: Effect of Temperature and Water/GHR Ratio on Mass and Energy Yield
Bibliographic record
Abstract
The Valle del Cauca region in Colombia is a significant producer of sugar cane, resulting in large quantities of agricultural residues (green harvesting residues (GHRs)). To ensure sustainable management of these residues, it is crucial to implement proper treatment and disposal technologies while also reusing waste to produce biogas, bioelectricity, or biofuels. The biomass hydrothermal carbonization process offers a means to convert these residues into useful products that serve as fuels or valuable energy materials. This thermal treatment involves the use of water as a solvent and reagent within the biomass's internal structure. In this study, sugar cane cutting residues were collected with relatively high moisture content of 8.5% wt. These residues were subjected to carbonization temperatures ranging from 200 to 300 °C, along with water/GHR ratios between 5/1 and 10/1. The properties of the resulting hydrocarbons were analyzed by using proximate and ultimate analysis. The objective was to produce hydrochar samples with the highest higher heating value (HHV) and energy density compared with the GHRs. The HHV value of the hydrochar showed a significant increase of 69.6% compared with that of the GHRs, reaching 43.5 MJ/kg. Besides, process parameters were optimized for mass yields, energy yields, and ash content. This exploration led us to investigate a new temperature range between 280 and 320 °C, allowing us to establish an optimal value for the hydrochar's properties.
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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 | 0.000 |
| 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, 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".