Assessing the Potential to Generate Heat and Electricity from the Wastes Produced in Alberta Industrial Heartlands
Bibliographic record
Abstract
Utilization and disposal of waste is a major concern in various jurisdictions in Alberta and Canada. The wastes are mostly comprised of municipal solid waste (MSW), agricultural residue (straw, livestock manure and on-farm dead), forest residue (roadside residues, mill waste) and waste heat from various industries. The amount of MSW as well as agricultural and forest residues available in the province of Alberta was estimated at 4.09 Mt/yr, 6.53 Mt/yr and 4.1 Mt/yr, respectively. Major portion of these wastes could be potentially diverted from being landfilled or burned to being utilized for energy production. This research focuses on Edmonton Industrial Heartland (AIH), in first phase, and the whole province of Alberta, in the second phase, to assess the utilization of waste material/energy for production of value-added products, particularly heat and electricity. This study includes development of extensive techno-economic models. Geographic information system (GIS) is as well used to identify the suitable locations for waste-to-energy conversion facilities via conducting exclusion, preference, and location-allocation analysis. Suitable locations are afterwards economically assessed for various waste conversion technologies along with optimization of size and identification of most optimal location(s). One waste-to-value added facility for AIH and 10 facilities for the province of Alberta are at the end recommended with exact geographical location and estimated cost of potential value-added products to be produced.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".