Development and comparative lifecycle assessment of various LDPE and HDPE production processes based on CO2 capture and utilization
Bibliographic record
Abstract
Low-density polyethylene (LDPE) and high-density polyethylene (HDPE) are some of the most commonly used materials worldwide. These polymers are typically produced through the polymerization of ethylene which is conventionally produced through the energy-intensive steam cracking of naphtha or ethane. However, the conventional production process for LDPE and HDPE results in significant greenhouse gas emissions (1.92 and 1.86 kg CO2/kg of polymer, respectively). \nHence, this work focuses on the design of alternative pathways based on CO2 capture and utilization (CCU) for polymer production. The proposed pathways for CCU-polymers are based on the conversion of CO2 to methanol, followed by the methanol-to-olefins (MTO) process to produce mainly ethylene, and consequently LDPE and HDPE from ethylene. The process design and simulation of the entire pathway are conducted in AspenPlus, while the life cycle assessment (LCA) is done through OpenLCA. \nThe LCA results showed that the CCU-MTO pathway is an environmentally attractive option, particularly in regions where renewable (low-carbon) electricity is more prominent, such as Quebec and Ontario, where negative CO2 emissions are achieved. This makes polymer production via the proposed method suitable for the permanent mitigation of CO2.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".