Numerical sensitivity analysis of temperature‐dependent reaction rate constants for optimized thermal conversion of high‐density plastic waste into combustible fuels
Bibliographic record
Abstract
Abstract The use of experimental rate constants for producing a high yield of liquid fuels from the pyrolysis of plastic waste is not widely accepted owing to a lack of compatibility between the different kinetic rate constants responsible for successful conversion reactions. In R software, the Arrhenius law can forecast the ideal combination of reaction rate constants and frequency factors and then perform sensitivity analysis on individual rate constants to estimate the selectivity and quantity of primary pyrolysis products. Sensitivity analysis is a way of determining the effectiveness of individual rate constants in the reaction. This research element is currently lacking in the literature for the cost‐effective valorization of plastics into combustible fuels. We are the first to use R software to perform sensitivity analysis on specific rate constants by reducing or raising their initial values to a point where maximum oil yield is attainable in the temperature range of 340–370°C. The primary focus was to save time and cost of extracting empirical rate constants from experiments to produce commercial‐scale pyrolytic oil. The H‐abstraction, chain fission, polymerization, and scission reactions were chosen due to the high availability of free radicals for maximum oil production. The oil recovery rate improved drastically to 90% at the end of processing time, while the number of by‐products gradually decreased. The k8 rate constant driven reaction is the best‐suited condition for industrial‐scale pyrolysis of high‐density plastics into liquid fuels, with 74% improvement in oil production and 14% improvement in light wax during sensitivity analysis.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".