Assessment of cleaner process options: A case study from petroleum refining
Bibliographic record
Abstract
Abstract Assessment of process changes to reduce, recycle or avoid wastes requires attention to systems which are broader than the immediate process; that is, it is necessary to take a life cycle perspective. Definition of the system boundary for such an assessment can be problematic in itself. A real case study is presented to illustrate the problem of assessing clean technologies: possible modifications to an alkylation unit at a UK refinery. The process uses hydrogen fluoride as alkylation catalyst, and generates fluoridic wastes which are hazardous and require treatment both on‐ and off‐site. Possible changes to avoid, reduce or enable partial recycling of the waste are identified, representing different levels of change in the process and therefore requiring assessment with different system boundaries. The different system definitions lead to differences in the ways data must be compiled for quantitative environmental life cycle assessment, and in the range of stakeholders explicitly or implicitly involved in assessing and implementing the changes. The case study demonstrates some of the less familiar challenges introduced by the “pollution prevention” or “clean technology” paradigms of chemical processing.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".