Total Capital Investment of plastic recycling plants correlates with energy losses and capacity
Bibliographic record
Abstract
Abstract Plastic pollution is a generational problem, and stakeholders are turning to chemical recycling as a potential solution. However, decision‐makers necessitate quick and reliable capital investment estimations to evaluate innovative technologies, especially in the early project stage, when limited historical data are available. To address this need, we built a database of 160+ chemical recycling plants, querying for nominal capacity, year and place of construction, total capital investment (TCI), number of long‐term jobs and opportunity of subsidies. Then, we compared conventional association of the advancement of cost engineering AACE class 5 estimation methods, with literature estimates, and commercial capital expenditure confidence intervals for pyrolysis, gasification, solvolysis, and selective dissolution. We demonstrate the unreliability of classic methods, and we propose ballpark correlations based on the plant capacity, or the energy loss. Chemical recycling plants suffer from poor economy of scale (with current technologies), and capacity is not always the best indicator for TCI estimation. Pyrolysis and gasification are energy‐driven technologies, and their TCI correlates very well (R 2 =0.91–0.92) with the total energy losses. Solvolysis and selective dissolution, instead, are at an earlier development stage, so cost engineers or researchers will have to accept less certain TCI vs capacity (R 2 =0.60).
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".