Concurrent 2. Presentation for: Techno-economic evaluation of blue hydrogen production with carbon capture and storage for onshore Eastern Australia
Bibliographic record
Abstract
Presented on Tuesday 17 May: Session 2 This techno-economic assessment models the feasibility of a greenfield blue hydrogen development with production capacity of 400 tons of hydrogen per day (TH2/day). At an assumed 85% CO2 recovery rate, the 400 TH2/day production scale equates to a need for approximately 1.241 million tons of CO2 per year (MtCO2/year) of subsurface CO2 storage capacity over an assumed project life of 25 years (approximately 31 Mt of total CO2 sequestered). The blue hydrogen production technology applied to the assessment is a Steam Methane Reformer (SMR) with two points of CO2 recovery (pre-combustion process stream amine scrubbing and post-combustion flue gas capture). The remaining key scope components are the CO2 compression and dehydration system, 65 km of CO2 distribution pipeline and a CO2 injection and storage hub comprised of three injection wells, three deep observation wells and three groundwater monitoring wells. The study includes the screening process applied to identify five high-grade depleted gas reservoirs in the Cooper Basin that are the CO2 storage candidates, and which ultimately define the project location. The economic evaluation of the project includes the definition of cost and operations estimates to determine a mean project case and then applies a Palisade @Risk probabilistic distribution model to key project inputs as to risk the project under various scenarios. The analysis concludes that the economic viability of large-scale, greenfield blue hydrogen projects in Australia is highly dependent on hydrogen sale price, Australian Carbon Credit Unit (ACCU) value, wholesale natural gas prices and capital cost efficiency. To access the presentation click the link on the right. To read the full paper click here
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.206 | 0.035 |
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".