Kinetic Modeling of the Biogenic Production of Coalbed Methane
Bibliographic record
Abstract
Biogenic production of coalbed methane under anaerobic conditions occurs through a large number of reactions involving a community of micro-organisms. We propose a kinetic scheme for this complicated reaction network using lumped species reacting in a series of enzymatic reaction blocks consisting of coal solubilization, hydrolysis, acidogenesis, acetogenesis, and methanogenesis. Among these pathways, acetoclastic methanogenesis is assumed to be dominant. Based on implications from experimental data, tryptone (a nitrogen rich nutrient used in the stimulation of methane production) is assumed to produce aromatic ring intermediates. Coal solubilization is described by a diffusion layer model. Monod kinetics are applied to model the enzymatic reaction rates, but the rate of methanogenesis is modeled with modified Monod kinetics to account for substrate inhibition. An analytical solution to the model is derived and its parametric sensitivity is investigated in different operating regions. Model parameters are estimated from data from various anaerobic bottle experiments conducted by us using nonlinear regression with the particle swarm optimization algorithm, and the model’s predictive ability has been validated for various coal samples from the literature, too. The predictive kinetic model thus established provides estimates of the concentration of products as well as intermediate species in the conversion of coal. The model can be used to optimize biogenic methane production from coal at different scales ranging from coreflooding experiments to the reservoir and field scales.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".