Techno-economic and Environmental Assessments of Replacing Conventional Fossil Fuels: Oil Sands Industry Case Studies
Bibliographic record
Abstract
Conventional fossil fuels are widely used, however there are growing concerns about the security of their supply, volatility in their prices and the environmental impacts of their extraction and use. The objective of this research is to investigate the potential for replacing conventional fuels in various applications, focusing on the Alberta oil sands industry. Such investigations require systems-level approaches able to handle multiple criteria, uncertainty, and the views of multiple stakeholders. To address this need, the following are developed: life cycle assessment (LCA) and life cycle costing models of polygeneration systems; a life cycle-based framework for multi-sectoral resource use decisions; and a method combining LCA and real options analyses to yield environmental and financial insights into projects. These tools are applied to options for utilizing oil sands outputs, both the petroleum resource (bitumen) and by-products of its processing (e.g., asphaltenes, coke), within the oil sands industry and across other sectors. For oil sands on-site use, multiple fuels are assessed for the polygeneration of electricity, steam and hydrogen, in terms of life cycle environmental and financial impacts; asphaltenes gasification with carbon capture and storage (CCS) is the most promising option, able to reduce greenhouse gas (GHG) emissions to 25% of those of current natural gas-based systems. Coke management options are assessed with the life cycle-based framework; the most promising options are identified as: Electricity generation in China through integrated gasification combined cycle; and, hydrogen production in Alberta, either for sale or use by the oil sands industry. Without CCS, these options have amortized project values ranging from $21 to $160/t coke. The application of the combined LCA and real options analyses method finds that uncertainty in natural gas and potential carbon prices over time significantly impacts decisions on coke management; the formulated decision tree identifies increases of 29% and 11% in the financial and GHG emissions performance, respectively, of the overall coke management project compared to pursuing the decision identified by the life cycle-based framework. While promising options for replacing conventional fossil fuels are identified through systems-level analyses, there are trade-offs to be made among the financial, risk and environmental criteria.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".