Chemical engineering and industrial ecology: Remanufacturing and recycling as process systems
Bibliographic record
Abstract
Abstract Climate change and resource scarcity are just two of the planetary crises that make radical socio‐economic change essential if human society is to be sustainable. Chemical engineering is a skill‐set that can make a unique contribution to the socio‐economic transition, going beyond new technological processes to provide a system‐level understanding of economic activities from the perspective of industrial ecology. This paper provides an example by applying process system analysis to the use, re‐use, remanufacturing, and recycling of material products. Unlike the ‘circular economy’ approach, the analysis starts from the stock of goods and materials in use in the economy and models the flows required to build up, operate, and maintain the stock. Metrics are developed to account for the effect of stock growth on demand for materials. The significance of the analysis is illustrated for four metals whose industrial ecologies are at different levels of maturity: lead, copper, aluminium, and lithium. Extending product life through re‐use and remanufacturing is crucial for resource efficiency, using labour to reduce demand for energy and non‐renewable resources. If end‐of‐life products are processed to recover individual elements, the cost penalties increase rapidly with the decreasing concentration of valuable materials and increasing number of materials in the mixture. Thus, shifting from a linear economy (make−use−dispose) to closed‐loop use of materials involves rethinking product design to reduce the number of materials used. Material substitution to reduce demand for scarce materials needs to look beyond equivalence of function to consider changing patterns of use in the regenerative economy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".