Recovery and Reuse of Valuable Chemicals Derived from Hydrothermal Carbonization Process Liquid
Bibliographic record
Abstract
Recovery and reuse of valuable chemicals from hydrothermal carbonization process liquid (HTC-PL) from tomato plant biomass (TPB) was conducted. Different HTC-PLs were characterized with FTIR and Py-GC-MS analyses revealing the presence of low molecular weight linear, cyclic, and aromatics compounds in the HTC-PL. Separation of these valuable chemicals by fractional distillation resulted in eutectic constrains. Solvent extraction separation followed by solvent recovery and reuse provided encouraging results. The non-polar portion of HTC-PLs were extracted by using n-hexane (C6H14), and diethyl ether (C2H5)2O solvents with 8.5 and 4.3% recoveries, respectively. Characterization by FTIR and Py-GC-MS revealed petrol fuel like materials in the extracts of C6H14 and (C2H5)2O, irrespective of higher boiling components. Blends of both non-polar extracts were flame tested revealing good burning characteristics with minimal smoke and residue. Bench scale spirit lamp tests showed the blend would be very useful for greenhouse winter heating. The polar extracts using methylene chloride (CH2Cl2) resulted in about 55% recovery. Py-GC-MS analysis revealed acetic acid and 5-Hydroxymethyl furfural (5HMF) majors in the extract along with related derivatives. 5HMF is a valued chemical and demonstrated to be a useful building block for many industrial applications, and flatform chemical for various synthesis. Other identified minor components of HTC-PL were vanillin, divinyl terephthalate, and syringol. After the extractions of polar and non-polar components in three steps, the HTC-PL residue was applied as nutrient source after maintaining pH (5.6) and concentration (TOC, 100–200 mg/L) to typical greenhouse plants. Plant growth was encouraging. The paper discusses all the potential valued reuse applications of HTC-PL in greenhouses without discharges, which contributes to environmental protection and economic benefits.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
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".