Techno Economic Assessment of the Gas to Liquid and Coal to Liquid Processes
Bibliographic record
Abstract
With the fluctuations in conventional crude oil production and uncertainty in its global price, alternative sources of liquid fuels from natural gas and coal are being considered. In addition, formulation of policies in several jurisdictions on phase-out of coal power plants due to climate change considerations has also created a need for the development of alternative utilization of coal. Gas-to-liquid (GTL) and coal-to-liquid (CTL) processes are two liquefaction technologies that respectively convert natural gas and coal to liquid fuels. There is very limited work on development of scale factors for estimation of capital cost of these plants. In this study, the economic potentials of the GTL and CTL processes are investigated. A case study for western Canada is conducted which has large deposits of coal and natural gas. The capital costs of the key equipment of the plants are estimated through development of cost scale-up factors. The production cost for a 50,000 bbl/day of liquid fuels from the GTL and CTL plant is estimated through development of data-intensive techno-economic models using bottom-up methodology. The developed scale-up factor for the GTL and CTL was found to be 0.7 and 0.65 respectively. For both plants, benefits of economey of scale is achieved at a capacity above 20,000 bbl/day. The production cost of the GTL and CTL process are 44.61 and 57.65 cent/lit respectively. On the other hand, when carbon capture and sequestration (CCS) was considered, the production cost of the CTL plant increases significantly. The potential usage of the GTL is attractive due to its simplicity and the relatively low capital investment. On the other hand, the CTL is complex due to the additional CCS and sulphur removal unit.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".