A new system using refuse-derived fuel as a feedstock for hydrogen and power co-generation: A techno-environmental assessment
Bibliographic record
Abstract
Refuse-derived fuel (RDF) offers a promising opportunity for Canada to proceed with its transition towards a circular economy. This study focuses on the development of a pseudo-steady-state process simulation model to integrate RDF processing and utilization technologies with the existing plants. Canada’s RDF is chosen as a potential feedstock and is coupled with the two cycles (Sulfure Iodine (S-I) thermochemical cycle and dual pressure loop power cycle) to produce carbon-free H 2 and electricity. The process modeling is undertaken in the Aspen Plus industrial software. An environmental and thermodynamic assessment of the plant including energy and exergy analyses is conducted. Several sensitiivty analyses are computed to predict the ideal operational parameters for optimized efficiency. A feed of 1300 kg/h RDF generates 96.76 kg of clean hydrogen and 382.24 kW of clean power. The results show RDF-to-power and RDF-to-H 2 energy efficiency of 22.19% and 25.27% respectively. Regarding the environmental performance, the results show that the proposed plant avoids 423.82 tons of CO 2 emissions per year for clean hydrogen production and 1685.45 tons of CO 2 emissions per year for clean power production. The overall performance of the developed system in terms of energy and exergy efficiencies is 35.91% and 42.46%. • The refuse-derived fuel serves as feedstock for H2 and power production. • The sulfur-iodine thermochemical cycle achieves 65.63% energy efficiency. • The system avoids 423.82 tons of CO₂ emissions per year for H2 production. • A total of 1685.45 tons of CO₂ emissions are avoided per year for clean power. • The overall energy and exergy efficiencies are 35.91% and 42.46%, respectively.
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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 | 0.000 | 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".