Dissolution behaviour of different rank coals in <scp>L</scp>‐glutamic acid <scp><i>N</i></scp>, <scp><i>N</i></scp>‐diacetic acid chelating agent: Implications to enhance coalbed methane recovery by acid stimulation
Bibliographic record
Abstract
Abstract Acid stimulation is a promising method in improving coal seam permeability by demineralization in coal cleats. However, using conventional acids such as HCl and mud acid (12 wt.% HCl + 3 wt.% HF) may cause clay breakage, migration, and precipitation, leading to formation damage. In this study, we investigated the effect of L‐glutamic acid N, N‐diacetic acid (GLDA) chelating agent treatment on the dissolution behaviour of lignite (Coal L) and anthracite (Coal A), collected from Er′Lian Basin and Qinshui Basin, China. Characteristics of the raw and acid treated coal samples, including mercury intrusion porosimetry (MIP) results as well as metal elements distribution analysis of reacted leachates, micromorphology, mineral composition, and mass change were taken into consideration. It was found that high‐rank Coal A is more suitable for chemical stimulation. For both Coal L and Coal A, 5 wt.% GLDA solution is preferred to avoid sticking reaction products. Total porosities of Coal L and Coal A were increased by 3.9 and 1.1 times, respectively. Based on the water chemistry analysis of the reacted solutions, we found that acidic condition is beneficial for clay minerals and carbonates dissolution, while alkaline condition is favourable for the dissolution of quartz. Besides, the dissolution of minerals can trigger the release of trace elements, which is a potential environmental risk that needs to be taken into consideration before its in‐situ application in coal fields.
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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".