MétaCan
Menu
Back to cohort
Record W7037201209

Development and comparative lifecycle assessment of various LDPE and HDPE production processes based on CO2 capture and utilization

2022· dissertation· en· W7037201209 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2022
Typedissertation
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsnot available
Fundersnot available
KeywordsLow-density polyethyleneNaphthaHigh-density polyethylenePolyethyleneProduction (economics)Life-cycle assessmentProcess (computing)
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.307
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueSpectrum Research Repository (Concordia University)Same topicCarbon dioxide utilization in catalysisFrench-language works237,207