Concurrent 2. Presentation for: Techno-economic evaluation of blue hydrogen production with carbon capture and storage for onshore Eastern Australia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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