Preliminary Engineering Study for Evaluating the Industrial Feasibility of the Chemical Recycling of PET through Glycolysis
Bibliographic record
Abstract
The department of chemical engineering of the University of Sherbrooke has established since more than 12 years a course of integration (Design of Industrial Chemical Processes) at final year of its programme; during this last year the students form working groups like in Engineering companies and, under the scientific and technical ‘coaching’ of their professors-engineers and their assistants proceed to the preliminary engineering of a chosen industrial process. This activity includes an intense computational effort to simulate the operation of all modules of the industrial production facility and leads to results which have a considerable research value and it is considered by interested investors for industrial applications. This work presents the results of this effort applied in the case of the chemical recycling of a plastic residue, the PET. Many programs of paper, metal, glass and plastic recycling have been implemented during the last 20 years. Plastic recycling is by far the most complex and it is still under heavy R&D. This is mainly due to the complexity of the formulations and the low raw materials cost in industry. Indeed, in the majority of the cases, the monomers of synthesis are less expensive than the recycled plastic which results mainly from mechanical recycling (shredding and extrusion). This work focuses on the chemical recycling by means of Glycolysis of one of the principal plastics used in the industry of food packing: the Poly-(Ethylene Terephthalate) (PET). The product obtained by glycolysis is the Bis-(Hydroxyl-Ethyl) Terephthalate (BHET) which is the building unit of the PET. The chemically recycled PET, said “food rank”, has a commercial value much higher than that of the mechanically recycled PET because of its higher purity, higher specifications and low risk of cross-contamination. In the United States, according to FDA's rules, the chemically recycled PET can be used in concentrations up to 25% in the manufacture of food packing. The results of this work show that the ideal capacity of production for a chemically recycled PET production facility is of 25 000 ton/y and the most favourable site, according to the market research and techno-economic evaluation, is the state of South Carolina, USA. The factory obtains a ROI of 11.41%. The sensitivity analysis shows that the parameters influencing more the profitability of the factory are: the selling price of the BHET, the production volume (output), as well as the cost of the raw material (used PET).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".