Effects of Process Water Recycling and Particle Sizes on Hydrothermal Carbonization of Biomass
Bibliographic record
Abstract
Hydrothermal carbonization (HTC) is a promising thermochemical process for the conversion of biomass with high moisture content, and therefore, interest in the development of continuous technology is growing. However, the development of continuous HTC systems requires the in-depth understanding of reaction kinetics, heat transfer mechanisms, exothermic or endothermic nature of the reaction, effect of operational parameters, recyclability of process water, and variability in biomass feedstock. The current paper discusses the effects of recyclability of process water and particle size on the product yield. To investigate the water recycling effect, HTC experiments were performed on sawdust using recycled water in a batch reactor. Moreover, to study the particle size effect, 3 small reactors containing specific particle sizes of biomass were placed in a larger reactor simultaneously to ensure consistency in the process condition. The results showed that the process water recycling increases the mass yield and higher heating value (HHV) of the solid product by 12% and 2%, respectively, after the first recycle. Physicochemical properties of hydrochar were studied by carrying out the ultimate and proximate, HHV, thermogravimetric coupled with Fourier transform infrared spectroscopy, and Brunauer–Emmett–Teller analysis, and results are discussed. Regarding the particle size study, higher mass yield, a decrease in heating value, and an increase in total organic carbon in the process water were observed by increasing the particle size.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".