Hydrothermal Conversion of Waste Biomass from Greenhouses into Hydrochar for Energy, Soil Amendment, and Wastewater Treatment Applications
Bibliographic record
Abstract
Solid hydrochar (HC) produced by hydrothermal carbonization (HTC) of tomato plant biomass from a greenhouse (GH) was assessed for different inhouse applications, including fuel, seed germination, and leached GH nutrient feed (GNF) wastewater treatment. Completed experiments showed encouraging results. HC was revealed to be an efficient renewable fuel, having peat-like characteristics with high heating value of about 26.0 MJ/kg and very low clinker forming potential. This would allow the use of HC as fuel for GH heating as a substitute to costly natural gas, or it could be commercialized after pelletizing. Experiments with soil application showed substantial potential for the produced HC in better seed germination of tomato plants. Another benefit from use of the produced HC is as a soil additive, which would also contribute to environmental emission reduction. Results suggest that the generated HC can remove about 6–30% of nutrients from leached-GNF wastewater. This would be an essential treatment in the reduction of nutrients from leached water from GH operations, and thus could prevent/reduce eutrophication. The exhausted HC after treatment application could then be reused for soil remediation. Overall, the paper highlights the potential applications of hydrothermal treatment in valorization of low-valued GH TPB waste, resulting in a circular economy.
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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".