Process optimization and techno-economic analysis of polyethylene terephthalate (PET) depolymerization in a non-aqueous alkaline environment for monomer recovery and reuse
Bibliographic record
Abstract
This study presents a comprehensive investigation into optimizing polyethylene terephthalate (PET) depolymerization under alkaline conditions using response surface methodology (RSM). The critical process parameters studied were temperature, NaOH concentration, and reaction time. In the first phase, the combined effect of ozone pretreatment and ultrasound assistance on depolymerization efficiency was examined, revealing a positive direct correlation. In the second phase, RSM optimization with ozone pretreatment with ultrasonication conditions achieved 83.58% terephthalic acid (TPA) recovery and a total monomer recovery of 92.14%. Notably, applying the RSM-optimized conditions to virgin PET samples without pretreatment yielded comparable results thus highlighting the importance of statistical optimization tools. Techno-economic analysis using RSM optimized conditions for plant capacities ranging from 2.4 to 72 MT/day identified a minimum selling price of $1,533 USD/MT for TPA at the largest making it competitive with virgin TPA and other depolymerization methods. Sensitivity analysis highlighted feedstock and equipment costs as primary economic drivers, revealing opportunities for cost optimization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".