Methodology to predict natural gas in loading and unloading of compressed natural gas (CNG) operations
Bibliographic record
Abstract
Exploiting stranded gas reservoirs, associate gas from offshore production platforms, condensate gas reservoirs may not be feasible using traditional approaches (i.e. pipelines) due to gas volumes, composition, location and/or climate. Marine Compressed Natural Gas (CNG) technology is a possible alternative; however, the required gas quality for CNG remains a challenge in commercial deployment. The optimal design conditions to safely and efficiently load, store, and unload CNG vessels are highly dependent on the quality of the gas. The objective of this work was to evaluate the impact of moderate/rich gases in CNG technology by performing dynamic simulations of the loading and unloading operations. It was demonstrated that existing Equations of State (EOS) are limited in accurately predicting the behaviour of the gas under load/unload conditions, particularly for gases with large heavy hydrocarbons content. Experiments with laboratory-synthesized gas samples were conducted using a PVT cell. The accuracy of the EOSs in predicting dew points, liquid dropout percentages, and gas densities was evaluated using the experimental data from the laboratory and literature. After tuning Peng-Robinson (PR) and Soave-Redlich-Kwong (SRK) equations to improve their predictions, their performance was evaluated using the HYSYS process simulator. A marked improvement in the EOS predictions was achieved by modifying a few EOS parameters. Dew point predictions were improved by adjusting binary interaction parameters (kij), the density predictions were improved by modifying the Peneloux parameters, and modifying both kij and Peneloux parameters enhanced the liquid dropout predictions. Dynamic simulations of the loading and unloading operations of a CNG tank were then performed to evaluate the effect of the heavy hydrocarbon content of the gases. Overall, removing all the heavier hydrocarbons appeared to produce very low temperatures during the unloading operation, while not removing these components caused an accumulation of liquids inside the tank at keel pressure. In addition, the simulation of the loading and unloading of a CNG tank was attempted in the laboratory using the PVT system; however, the attempt was unsuccessful as the system would require major modifications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".