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Record W2528852552

Investigation on using Supercritical Carbon Dioxide as Desorbing and Reaction Medium in the Surfactant Production Process

2007· dissertation· en· W2528852552 on OpenAlexaboutno aff
Yuanping Yuan

Bibliographic record

VenueUWSpace (University of Waterloo) · 2007
Typedissertation
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSupercritical carbon dioxidePulmonary surfactantSupercritical fluidCarbon dioxideProcess (computing)ChemistryChemical engineeringProcess engineeringEnvironmental scienceOrganic chemistryComputer scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

To date, an estimated 70% of energy consumed comes from fossil fuels, such as coal, oil and natural gas. The major source of sulfur dioxide (SO2) emissions comes from \ncombustion of these fossil fuels. Sulfur dioxide is a significant pollutant, because it and its higher oxidation product (SO3) react with moisture in the atmosphere to produce sulfuric acid. This results in acid rain, which comes back to earth and affects people, animals, and vegetation. Therefore, the governments of Canada, US and European \ncountries are issuing stricter and stricter regulation to control SO2 emissions. \n \nIn conventional SO2 removal processes, lime or limestone scrubbers are used, but they \nrequire large amounts of water and enough landfill sites to deal with the solid wastes. Previous attempts were made in our laboratory to recover SO2 adsorbed on activated carbon to produce sulfuric acid using non-aqueous solvents. Unfortunately, in this adsorption/distillation process, the SO2 recovery was low, as was the quality of sulfuric acid, that could not be marketable. The topic of this thesis was then conceived as an attempt to first recover SO2 via SO3 formation using supercritical carbon dioxide instead \nof water or non-aqueous flushing agents (desorption step) and then to use the recovered SO3 to produce linear alkylbenzene sulfonates (LAS), the main component of detergent. \n \nIn the adsorption and oxidation experiments of this project, charcoal activated carbon \n(AC) was used to adsorb SO2 and to catalyze SO2 oxidation. The process started with a \nsimulated flue gas, 3500 ppm SO2, 5% O2, balanced with N2. When the simulated flue \ngas passed through the activated carbon bed reactor, more than 95% of SO2 was oxidized \nto SO3. \n \nIn the desorption process, SO3 contacted with the AC bed was removed using supercritical carbon dioxide (SCCO2) and 95% sulfur removal was achieved at \nappropriate operating conditions, for example, for a carbon bed preheated at 250°C for 6 h, and flushed by recycled SCCO2. \n \nThe LAS production experiments consisted in reacting liquid linear alkylbenzene (LAB) \nwith the recovered SO3 in an absorption column. Ceramic filters and glass beads were \nused in the absorption columns to break up the gas bubbles and increase the contact time between the gas and the liquid absorbent. When staged pressure columns were used and when LAB was heated to 40°C, nearly 95% of SO3 reacted with LAB to produce LAS.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.226
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2007
Admission routes1
Has abstractyes

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