Estimation of Gas Adsorption Capacity in Coal: A Review and an Analytical Study
Bibliographic record
Abstract
Adsorption plays an important role in carbon dioxide sequestration and methane recovery processes in deep coal seams. If the effects of coal physical properties on its gas adsorption capacity are considered, it increases with vitrinite content and pressure and reduces with liptinite, mineral matter, moisture contents, and temperature and follows a U-shaped variation with carbon content. Furthermore, CO2 has higher adsorption capacity compared to other gases. There are two main methods to estimate the amount of gas adsorbed in a coal seam, which are called direct and indirect methods. The latter method is more common for coal. In the direct method, the volume of gas released from a coal mass into a sealed desorption canister is measured, and under the indirect method, adsorption isotherms and empirical relations are used. However, any of those methods are unable to count the effect of swelling-induced strain and, therefore, fail in measuring the absolute adsorption in coal. Moreover, among various adsorption models, Langmuir and Dubnin-Radushkevich (D-R) equations are the most widely using models. However, since gas can be absorbed through both adsorption and absorption processes for coal, it is important to have an additional term to count the missing absorption mechanism in both the Langmuir and D-R models. Finally, a new descriptive model for gas adsorption capacity of coal as a function of effective factors is proposed. The new model is based on the existing D-R equation, which was modified by inserting a new expression for the term of micropore capacity. Two types (CO2 and N2) of gas adsorption data for coal from five different locations (British Colombia and Alberta in Canada and Victoria, Sydney and Bowen in Australia) at three different temperatures (273, 296.5, and 318 K) were considered for the model development. According to the model results, new gas adsorption equations can fairly well accurately predict the adsorption capacity in coal.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".