Systematic approach to the design, modeling, and techno-economic-environmental analysis of CO2 capture technologies as part of the National CCUS Assessment Framework (NCAF)
Bibliographic record
Abstract
Given the commitment to reaching net-zero emissions by 2050, the deployment of carbon capture, utilization, and storage (CCUS) technologies will be instrumental in reaching gigatonne-scale CO 2 mitigation. High costs driven by economies of scale, CO 2 partial pressures, and large energy demands, along with need for substantial new CO 2 transportation and storage infrastructure, are key barriers to large-scale the deployment of CCUS. This paper introduces the overall National CCUS Assessment Framework (NCAF) platform and its key elements to provide context on how it can be utilized to facilitate strategic planning of CCUS infrastructure at the regional to national scale. The NCAF platform, comprised of 5 components, combines rigorous datasets, costing and life cycle assessment (LCA) methods, optimization models, and visualization methods across the whole CCUS value chain. This paper focuses on the CO 2 Capture Modeling and Costing/LCA Tool providing details on overall approach, development steps, and the application of the techno-economic-environmental machine learning (ML) models to industry archetypes. Preliminary sensitivity and industry analysis show the robustness of the ML models, providing quick and accurate costs and environment burdens. Key parameters including flue gas flow rate and composition, capture rate, and product CO 2 pressure are explored, highlighting the ideal operating conditions when considering solvent-based post-combustion CO 2 capture. An exploratory analysis of over 300 Canadian emitting facilities provides practical information about how costs and overall global warming potential (GWP) vary between industry type, facility location, and production scale. Subsequent studies will focus on large-scale case studies to simultaneously determine and minimize the total cost of the entire CCUS value chain – CO 2 capture, transport, and storage.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".