MétaCan
Menu
Back to cohort
Record W4200452812 · doi:10.1021/acs.iecr.1c04211

A New Process for Peracetic Acid Production from Acetic Acid and Hydrogen Peroxide Based on Kinetic Modeling and Distillation Simulation

2021· article· en· W4200452812 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

VenueIndustrial & Engineering Chemistry Research · 2021
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsPeracetic acidHydrogen peroxideSulfuric acidChemistryAcetic acidMicroreactorCatalysisHydrolysisKineticsInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The reaction kinetics of peracetic acid from acetic acid and hydrogen peroxide in a helical capillary microreactor was investigated and modeled via regression of the measured conversion rate of hydrogen peroxide. It was found that the activity of the proton in the sulfuric acid solution played a significant role in the synthesis as a catalyst. The activities of the proton were calculated with the ELECNRTL equation and used to obtain a kinetic model with apparent activation energies of peracetic acid synthesis and hydrolysis of 53.63 and 54.45 kJ/mol, respectively. A new continuous process for industrial manufacture of peracetic acid solution (30 wt %) was proposed, where the reacted solution from the microreactor is distilled to separate unreacted reactants and sulfuric acid, which are recycled as a feed. The feasibility of the new process was validated by Aspen Plus simulation and economic assessment.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.075
GPT teacher head0.322
Teacher spread0.247 · 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