Effect of deep eutectic solvent on combustion characteristics of lignite under thermal treatment conditions
Bibliographic record
Abstract
Abstract The substantial moisture content of lignite imposes considerable constraints on its deep processing and subsequent utilization. A deep eutectic solvent (DES), synthesized from choline chloride (ChCl) and zinc chloride (ZnCl₂), was used to upgrade lignite under thermal treatment conditions. The effects of the DES molar ratio and its addition on the physicochemical and combustion characteristics of lignite were systematically analyzed at 280°C. The results demonstrated that the moisture content of lignite decreased from 22.63% to 5.18%, and then to 6.05%, as the molar ratio of DES was adjusted from 1:1 to 1:3, with the addition of 3 g of DES. This suggests that a DES molar ratio of 1:1 is more effective for lignite upgrading. As the DES addition increased from 1 g to 3 g, the moisture content of lignite decreased from 9.19% to 5.18%. As the molar ratio of ZnCl₂ in DES increased, the maximum combustion rate (V max ) and ignition index (C) of the upgraded coal sample gradually decreased, indicating that lignite upgraded with DES at a 1:1 molar ratio exhibited good combustion performance. The sample treated with 3 g of DES demonstrated the best overall combustion characteristic index ( S = 1.8), optimal ignition index (C), and combustion index ( R j ), when the DES addition increased from 1 g to 3 g. Compared to raw coal, the activation energy of the upgraded lignite was lower ( E ₐ = 63.99 kJ/mol), indicating enhanced combustion reactivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".