A synopsis on designing for multi-lifecycle in chemical engineering and the potential impacts on the attainment of circular economy in Africa
Bibliographic record
Abstract
Previous studies have shown that over seventy percent of product and process characteristics are determined at the design stage. Systems design goes a long way in determining not only the ecological footprint of a product but also that of the process that produces it. Over the last five decades, scholars and professionals have intensified effort at developing sustainable design approaches to minimizing resource use intensity of our industrial processes and products. Similar efforts were also targeted at waste minimization at various stages of the product life cycle. This study investigated the potential impacts that application of design for multi-lifecycle concepts in the chemical engineering field can have in the attainment of circular economy goals in Africa. This study involved a comprehensive review of various issues surrounding circular economy models and implementation, sustainable design concepts, and African socio-cultural characteristics. The study then zeroed in on design for multi-lifecycle concept, its applications, requirements, strengths and weaknesses. Taking cognizance of African sociocultural context, an investigation was then made into how and where design for multi-lifecycle (DfML) concept can be applied in key areas of chemical engineering field, and how application of DfML can foster or impair the attainment of circular economy goals in Africa. Preliminary results pointed to potentially huge success of DFML application in achieving sustainable circular economy in Africa under collaborative effort from governments, supply chain role players, and consumers. Unwavering public education and modern engineering training would also be essential before DFML would be able to deliver the desired circular economy dividend.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".