Impacts of geological and operational parameters on the performance of in-situ coal gasification using linked vertical wells
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Bibliographic record
Abstract
In-situ coal gasification (ISG) is a technology that converts underground coal into syngas, which can be used for power generation or as a chemical feedstock for urea production. ISG technology eliminates the need to construct surface gasification facilities or undertake mining activities, mitigating potential air pollution and reducing associated health and safety risks. And it can be effectively employed to repurpose abandoned coal mines. In this study, a numerical model for ISG using the linked vertical well (LVW) method has been developed based on some coal seam data from the Leigh Creek coal mine in South Australia. The aim is to investigate the impacts of operational parameters (such as the space between the injection well and production well, pressure difference between the injection well and the production well, and injection rates) and geological parameters (such as coal seam depth, coal seam thickness, and coal permeability) on the heating value of syngas produced. The model incorporates a series of complex chemical reactions, including drying, pyrolysis, gasification and combustion. The predicted results have been compared with the field experimental data from a Leigh Creek ISG test project. The simulations revealed that for the investigated LVW processes in Leigh Creek brown coal mines, the produced heating value of syngas in most cases is around 8MJ/m 3 . The injection pressure primarily influences the cavity pressure during ISG operations. The pressure difference between the injection and product wells is crucial for controlling the injection rate. Higher injection rates lead to a faster decline in the unit heating value of syngas produced, but a higher total energy output. The investigated coal seam’s depth and thickness have minimal impacts on the heating value. Decreased coal permeability impedes air injection and significantly reduces the reactions and syngas heating value.
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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 it