Gas to Liquids Techno-Economics of Associated Natural Gas, Bio Gas, and Landfill Gas
Bibliographic record
Abstract
Methane is the second highest contributor to the greenhouse effect. Its global warming potential is 37 times that of CO2. Flaring-associated natural gas from remote oil reservoirs is currently the only economical alternative. Gas-to-liquid (GtL) technologies first convert natural gas into syngas, then it into liquids such as methanol, Fischer–Tropsch fuels or dimethyl ether. However, studies on the influence of feedstock composition are sparse, which also poses technical design challenges. Here, we examine the techno-economic analysis of a micro-refinery unit (MRU) that partially oxidizes methane-rich feedstocks and polymerizes the syngas formed via Fischer–Tropsch reaction. We consider three methane-containing waste gases: natural gas, biogas, and landfill gas. The FT fuel selling price is critical for the economy of the unit. A Monte Carlo simulation assesses the influence of the composition on the final product quantity as well as on the capital and operative expenses. The Aspen Plus simulation and Python calculate the net present value and payback time of the MRU for different price scenarios. The CO2 content in biogas and landfill gas limit the CO/H2 ratio to 1.3 and 0.9, respectively, which increases the olefins content of the final product. Compressors are the main source of capital cost while the labor cost represents 20–25% of the variable cost. An analysis of the impact of the plant dimension demonstrated that the higher number represents a favorable business model for this unit. A minimal production of 7,300,000 kg y−1 is required for MRU to have a positive net present value after 10 years when natural gas is the feedstock.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".