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Record W4306398769 · doi:10.3390/pr10102094

An Intensified Green Process for the Coproduction of DMC and DMO by the Oxidative Carbonylation of Methanol

2022· article· en· W4306398769 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProcesses · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsUniversity of British Columbia
FundersKing Saud University
KeywordsDimethyl carbonateMethanolEnvironmentally friendlyProcess engineeringRenewable energyChemistryCarbonylationPulp and paper industryEnvironmental scienceWaste managementOrganic chemistryCatalysisEngineering

Abstract

fetched live from OpenAlex

Dimethyl carbonate (DMC) is an eco-friendly and sustainable compound with widespread industrial applications. Various extensive routes have been exploited in the chemical industry to produce DMC. However, these routes have several environmental and energy drawbacks. In this study, a promising novel industrial scheme for the synthesis of DMC via the oxidative carbonylation of vaporized methanol with dimethyl oxalate (DMO) as a byproduct is investigated. A methanol conversion of 81.86% and a DMC selectivity of 83.47% were achieved using an isothermal fixed-bed reactor at 130 °C. The DMC is withdrawn at a purity of >99 mol% via pressure-swing azeotropic distillations. Heat integration was performed to optimize energy consumption, reducing the energy requirements by 28%. An economic evaluation was performed for estimating the profitability via cash-flow diagrams, predicting a payback period of 3.7 years. The proposed green process exhibits several benefits, including high profitability and being environmentally friendly. It also eliminates the use or production of hazardous materials, and it enhances safety characteristics.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.030
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.281
Teacher spread0.258 · 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