Mathematical modeling of reversible and irreversible adsorption dynamics during CO2 storage in coal formation
Bibliographic record
Abstract
Coal is widely recognized as an effective CO 2 storage medium due to its high adsorption capacity. Compared to other rock types, coal offers significantly greater CO 2 adsorption potential, resulting in a larger storage capacity. However, it is well-known that CO 2 adsorption in coal does not occur abruptly or instantaneously but rather as a function of time. Specialized numerical modeling is required for understanding the CO 2 storage in coal due to its unconventional formation properties and complex storage mechanisms. Previous mathematical models lack consideration of time and space in CO 2 adsorption within coal formations. Additionally, the combined effects of injected CO 2 pressure and gas concentration on adsorption potential and storage capacity have not been clearly addressed or incorporated into mathematical models. This study focuses on modeling CO 2 adsorption in coal systems using sophisticated computational C# programming. Previously proposed mathematical models each addressing adsorption, pressure, concentration, and gas storage separately have been coupled to provide a comprehensive understanding of CO 2 adsorption in coal formations. The coupled model incorporates pore-scale surface heterogeneity and time-dependent adsorption behavior. The core novelty is the integration of multiple, previously isolated mathematical models (for adsorption, pressure, concentration, and gas storage) into a single, comprehensive coupled model. This holistic approach provides a more realistic and complete picture of the system's behavior. It also accurately defines the adsorption mechanism, accounting for both reversible and irreversible adsorption. The computational implementation of this model allows for the examination of CO 2 adsorption across time scales, ranging from micro to macro. Adsorption and mechanistic parameters in coal formations vary non-linearly with time. • The primary achievement of the article is the development of a single, comprehensive numerical model that integrates multiple, previously isolated mathematical models (for adsorption, pressure, concentration, and gas storage). • The model specifically addresses previous shortcomings by incorporating the effects of time, space, injected CO 2 pressure, and gas concentration on adsorption. • Reversible and irreversible adsorption depends on Injected CO 2 concentration which is directly linked to pressure strength. Quantity of irreversible adsorbed CO 2 is lower than reversible adsorbed CO 2 . • There is non-uniformity in CO 2 adsorption at initial stage and this effect diminishes after achieving certain CO 2 storage capacity, demonstrating a time-dependent adsorption mechanism. • The irreversible fraction indicates permanently sequestered CO 2 , while the reversible portion must be monitored for potential leakage. The model can directly inform the design and optimization of real-world carbon sequestration projects in coal formations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".