Chapter 13. Metallic Wastes into New Process Catalysts: Life Cycle and Environmental Benefits within Integrated Analyses Using Selected Case Histories
Bibliographic record
Abstract
For new technologies to become market competitors, they must operate substantially cheaper than their competitors or achieve outcomes that are difficult by current methods. Classical life cycle analysis (LCA) focuses on salient ecological impacts but bypasses key economic aspects and does not assign quantifiable benefits. This chapter factors in the benefits of environmental protection, reduced CO2 emissions, and the environmental impacts of oil extraction and fuel production using a well-to-gate (also known as cradle-to-gate) LCA, as well as the economics involving the mitigation of landfill gate fees for waste resources and social cost of carbon. The case histories evaluated involve catalysts bio-refined from wastes for application in cleaner extraction, upgrading, and processing of heavy fossil and pyrolysis bio-oils and comparisons to their commercial counterparts. Each case history material was analysed with a commercial catalyst and a bio-catalyst assessed as an alternative based on oil ratios (%eq. of g: g). Pyrolysis bio-oils from waste wood and algal sources were found to be upgradable successfully using both catalysts. They produce carbon-neutral fuels because of carbon sequestration during photosynthetic biomass growth, and the bacterial components supporting the catalyst become assimilated into the fuel.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".