Integration of Hydrogen Technology into Large Scale Industrial Manufacturing in Ontario
Bibliographic record
Abstract
Power-to-Gas is particularly applicable in Ontario’s energy market, due to the abundance of curtailed renewable energy. During off peak hours this results in not only low carbon, but low-cost electricity making hydrogen generation a highly profitable and environmentally friendly venture. Despite the benefits listed above, there has yet to be a full-scale adoption of Power-to-Gas technology both globally and in the local market. This eliminate this hesitation there is a requirement for diverse, profitable proof of concept installations and a public uncertainty regarding the inherent safety of the technology. It is the objective of this thesis to address these concerns by demonstrating the versatility of hydrogen in different energy system configurations, to show how layered revenue streams can produce profits in the face of policy uncertainty and by outlining the risks and control methods available to mitigate the safety concerns associated with Hydrogen. \nThe first paper presented in this thesis will address the question of whether a business case with strong financial returns is possible for a finished goods manufacture. Here we demonstrate the potential to capitalize on multiple revenue streams under a single investment and highlight some of the ancillary assets including reduction in air pollution and balance of the electrical grid. This design was developed for an automotive manufacturer requiring a total capital investment of $2,620,448 and resulting in a payback period of 2.8 years. Based on a sensitivity analysis, the annual revenue for selling hydrogen at $1.5 to $12 per kgH2 can sum to $54,741 to $437,928. In the modelled carbon tax program, CO2 allowances can be sold at $18 to $30 per tonne CO2 and the model predicts a CO2 offset of 2359.7 tonnes. \nThe second paper develops a case study that further expands on the use of a single pathway, the is the use of hydrogen enriched natural gas. This paper analyzes the integration of an electrolyzer unit into a manufacturer’s CHP microgrid and both explores the impact a carbon tax has on its feasibility and carries out a failure mode and effects analysis to highlight the safe nature of the technology. Currently realizable capital incentives can see IRRs as high as 13.76% with net present values of approximately $750,000. To realize financial feasibility, the carbon price in Ontario must achieve or exceed a minimum of 60$/ton CO2e. In all economically feasible, cases the system operating under an optimal storage coefficient and operational limit produced an emission offset greater than 3000-ton CO2 per year.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".