Optimizing hydrothermal carbonization for enhanced energy production from algal biomass with high moisture content
Bibliographic record
Abstract
Abstract Recently, the depletion of fossil fuels has become an issue, prompting the search for sustainable alternatives. Algal biomass has gained considerable attention as a promising renewable energy source because of its high production efficiency and adaptability to external environment. However, its high‐moisture content escalates the energy requirement during the thermal drying process in algal biomass production. Thus, we proposed a new energy production system using hydrothermal carbonization, which requires no pretreatment even for high moisture content biomass, making it compatible with such materials. Herein, we investigated the decrease in moisture content of algal biomass through hydrothermal carbonization and its effect on the energy production and energy balance of algal biomass. The results showed that hydrothermal carbonization at 240°C for 3 h produced hydrochar with a moisture content of 34.6%. It was found that it was due to changes in surface structures, such as CH, CO, and OH functional groups, using scanning electron microscopy (SEM) and Fourier transform infrared (FT‐IR) analysis. However, the greatest reduction in production energy, 45%, was achieved at 240°C for 4 h. The optimal energy balance was obtained for hydrothermal carbonization at 220°C for 4 h, for which energy production was 2.7 times more efficient than that achieved by conventional methods.
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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".