Feasibility Assessment of Electro-Thermal Energy Storage Adoption in Alberta’s Steam-Intensive Industries for Industrial Decarbonization
Bibliographic record
Abstract
Alberta’s steam-intensive industries, such as oil sands, chemical manufacturing, and pulp and paper, are critical to the province’s economy but contribute significantly to greenhouse gas emissions due to their reliance on fossil fuel-based steam generation. This study investigates the feasibility of adopting Electro-Thermal Energy Storage (ETES) systems to decarbonize these industries and enhance renewable energy integration in Alberta. Through a mixed-methods approach, including literature review, quantitative data analysis, and techno-economic modeling, the research identifies key steam-intensive sectors, assesses their thermal demands, and evaluates the technical and economic viability of ETES. Findings indicate that commercial ETES technologies, such as molten salt and concrete/rock storage, can meet the high-temperature steam requirements (180–565°C) of Alberta’s industries, offering up to an 80–90% reduction in emissions when powered by renewable electricity. This encompasses a broad range of electrification technologies. However, these estimates are based on modeled best-case scenarios and vendor projections rather than real-world operational data. Economically, ETES adoption could yield significant carbon cost savings under Alberta’s Technology Innovation and Emissions Reduction (TIER) regulation, with an estimated annual abatement of 33.4 million tonnes of CO2 for the oil sands sector alone. However, high capital costs and substantial electricity requirements highlight the need for robust renewable energy infrastructure and supportive policies. The study proposes a strategic roadmap for ETES deployment, emphasizing pilot projects, policy incentives, and grid enhancements. Despite data limitations and the emerging nature of ETES, this research underscores its potential as a transformative solution for industrial decarbonization, aligning with Alberta’s sustainability goals and the United Nations Sustainable Development Goals (SDGs) for clean energy, climate action, and industry innovation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".