Power-to-Gas Implementation for a Polygeneration System in Southwestern Ontario
Bibliographic record
Abstract
Canada has stockpiles of waste petroleum coke, a high carbon waste product leftover from oil production with little positive market value. A polygeneration process is proposed which implements “power-to-gas” technology, through the use of electrolysis and surplus grid electricity, to use waste petroleum coke and biomass to create a carbon monoxide-rich stream after gasification, which is then converted into a portfolio of value-added products with the addition of hydrogen. A model implementing mixed-integer linear programming integrates power-to-gas technology and AspenPlus simulates the polygeneration process. The downstream production rates are selected using particle swarm optimization. When comparing 100% electrolysis vs. 100% steam reforming as a source of hydrogen production, electrolysis provides a larger net present value due to the carbon pricing introduced in Canada and the cost reduction from removal of the air separation unit by using the oxygen from the electrolysers. The optimal percent of hydrogen produced from electrolysis is about 82% with a hydrogen input of 7600 kg/h. The maximum net present value is $332 M when over 75% production rate is dimethyl ether or $203 M when the dimethyl ether is capped at 50% production. The polygeneration plant is an example of green technology used to environmentally process Canada’s petroleum coke.
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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 | 0.000 |
| 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 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".