Hydrothermal Carbonization of Fruit Wastes: A Promising Technique for Generating Hydrochar
Bibliographic record
Abstract
Hydrothermal carbonization (HTC) is a useful method to convert wet biomass to value-added products. Fruit waste generated in juice industries is a huge source of moist feedstock for such conversion to produce hydrochar. This paper deals with four types of fruit wastes as feedstocks for HTC; namely, rotten apple (RA), apple chip pomace (ACP), apple juice pomace (AJP), and grape pomace (GP). The operating conditions for HTC processing were 190 °C, 225 °C, and 260 °C for 15 min. For all samples, higher heating value and fixed carbon increased, while volatile matter and oxygen content decreased after HTC. Except for ACP, the ash content of all samples increased after 225 °C. For RA, AJP, and GP, the possible explanation for increased ash content above 225 °C is that the hydrochar increases in porosity after 230 °C. It was observed that an increase in HTC temperature resulted in an increase in the mass yield for RA and GP, which is in contrast with increasing HTC temperature for lignocellulose biomass. Other characterization tests like thermogravimetric analysis (TGA) and scanning electron microscopy (SEM) also showed that the HTC process can be successfully used to convert fruit wastes into valuable products.
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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".