Application of a New Multicomponent Gas Adsorption Model to Coal Gas Adsorption Systems
Bibliographic record
Abstract
Summary A new multicomponent adsorption model is proposed for application to coal gas adsorption systems. The model is derived by combining the vacancy solution and Dubinin-Polanyi theories. Applications of the new adsorption model include the modeling of multicomponent adsorption processes associated with primary and enhanced coalbed methane recovery (ECBM). In the new model, the adsorbed phase in the single-component (pure) adsorption system is treated as a binary mixture of a singlecomponent gas with a hypothetical "vacancy" species, which also occupies adsorption space. The adsorption system is modeled as equilibrium between the gaseous phase and the adsorbed-phase vacancy solution. The Dubinin-Astakhov (D-A) equation is used to generate activity coefficients, as a function of the degree of porefilling, for the pure component gas in the binary (adsorbate+vacancy) adsorbed-phase mixture. The Wilson equation is chosen to fit pure component (D-A-derived) activity coefficient curves by optimizing the binary interaction coefficients. These binary interaction coefficients are then used to predict multicomponent adsorption equilibrium, although only the case of binary adsorption is modeled here. The adsorbed phase mixture for binary gas adsorption is treated as a ternary mixture of the two pure component adsorbates and the hypothetical vacancy species. Binary gas adsorption equilibrium is described by equilibrium between the gaseous components and the components in the adsorbed phase solution. Adsorbed-phase activity coefficients are calculated from the Wilson equation, with the binary interaction coefficients obtained from pure component adsorption data. Thus, only pure component adsorption data are required to make binary and multicomponent adsorption predictions with the new model. Two binary (CH4+CO2) gas adsorption experimental data sets with coal as the adsorbent and one binary (CH4+C2H6) gas adsorption data set with activated carbon as the adsorbent are used to test the predictions of the new model. In most cases the new model is able to predict binary gas adsorption accurately. The poor fit of the Wilson equation to the D-A-derived activity coefficients for some pure component data suggests that some improvement in model predictions could be made with the choice of a different activity coefficient equation. A unique feature of the current model is the ability to predict multicomponent gas adsorption at different temperatures from the pure component adsorption data collected at a single temperature. The temperature independence of pure component "characteristic" curves, as demonstrated in Dubinin-Polanyi theory, allows pure component adsorption to be predicted for a range of temperatures. These pure component data can then be used in modeling binary or multicomponent adsorption data at various temperatures. This is demonstrated for one experimental binary gas adsorption data set.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".