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Record W7155589025

Development of Membrane Technology for the Separation of Azeotropic Refrigerant Mixtures

2024· dissertation· en· W7155589025 on OpenAlexaboutno aff
Abby Noelle Harders

Bibliographic record

VenueKU ScholarWorks (The University of Kansas) · 2024
Typedissertation
Languageen
FieldEngineering
TopicMembrane Separation and Gas Transport
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantRefrigerationAir conditioningEconomic shortageGlobal warmingWork (physics)Global-warming potentialMontreal Protocol
DOInot available

Abstract

fetched live from OpenAlex

Rising temperatures across the globe have caused a surge in demand for cooling technologies—the global number of air conditioners will increase from 1.6 billion today to 5.6 billion in 2050, which is equivalent to 5 air conditioners being sold every second over the next 26 years. While access to air-conditioning and refrigeration is imperative for the survival of people in Earth’s hottest regions, the use of refrigerants has an environmental impact. The same compounds that provide relief from increasing global temperatures are some of the most potent contributors to global warming. It is estimated that 20% of global energy consumption results from the demand for refrigeration and air conditioning. Today’s generation of refrigerants, hydrofluorocarbons, can have global warming potentials up to 4000 times higher than CO2. The negative environmental impact of refrigerants has led to recent legislation such as the American Innovation and Manufacturing Act that requires a scheduled phasedown in hydrofluorocarbon refrigerant production over the next two decades. Although the production of these refrigerants is decreasing, new units designed for hydrofluorocarbons are still being sold into the market and existing units require hydrofluorocarbons to be serviced. A process for separating multi-component, azeotropic mixtures is urgently needed to account for the anticipated supply shortages in the refrigerant market and provide a stream of recycled refrigerant. This work investigated membrane technology for the energy-efficient separation of the refrigerant mixture R-410A, a widely used refrigerant for residential cooling and heat pump applications. R-410A is an azeotropic, 50-50 wt% mixture of difluoromethane and pentafluoroethane. Membrane separation offers a more energy efficient alternative to traditional separation processes like distillation since it is not reliant on phase-change or high temperature. Mixtures can be separated with membranes based on differences in both solubility and diffusivity. High performing polymers exhibit high permeability, a measure of polymer throughput for a given mixture component, and selectivity, the ratio of permeabilities for a given mixture. Polymers resistant to physical aging and plasticization are required for real-world utilization. This work has identified fluorinated, amorphous polymers to be promising materials for the separation of refrigerant gases due to high permeability, selectivity, resistance to plasticization, and stable separation performance over time. Investigation methods for polymeric materials included pure-gas permeability and mixed-gas permeability with the pressure-rise method, solubility with the gravimetric method, and diffusivity analysis with Fickian models. The scale-up of promising materials was accomplished through the fabrication of composite hollow fiber membranes. Amorphous copolymer of perfluoro(butenyl vinyl ether) and perfluoro(2,2-dimethyl-1, 3-dioxole) have been identified to provide a tunable, size-driven separation of R-410A. Increasing the amount of the dioxole-containing monomer increases the free volume of the polymer, leading to a sizeable increase in permeability of the smaller difluoromethane refrigerant. The lag in the pentafluoroethane permeability leads to an increase in membrane selectivity until a critical volume is reached. Beyond the critical volume, the permeability of both gases increases and the selectivity decreases. The findings indicate that fluorinated copolymers can be designed with optimal free volume to maximize both permeability and selectivity for refrigerant separations. In addition to amorphous perfluoropolymers, copolymers consisting of a perfluorinated monomer and a non-fluorinated vinyl acetate monomer were identified to further improve the separation of R-410A. The combination of high free volume from the perfluoropolymer and the chain mobility of the vinyl acetate monomer resulted in a highly diffusivity-driven separation. Composite membranes of ionic liquids and perfluoropolymers were also synthesized to assess the impact of ionic liquid on separation performance. The introduction of ionic liquid into the system resulted in an improved solubility selectivity that provides an additional design strategy to separate refrigerants. Amorphous perfluoropolymers were scaled up through the development of composite membranes of porous hollow fiber supports with a selective layer of perfluoropolymer. Predictive correlations to guide hollow fiber coating were developed based on physical properties of the coating solution. A continuous, reel-to-reel coating apparatus was developed capable of forming submicron coatings on porous hollow fiber supports. The fabricated hollow fibers were used to separate R-410A to a purity > 95 mol% for HFC-32 in a single pass. The large-scale separation of R-410A with fluorinated polymers was modeled in ASPEN Plus with the MEMSIC software tool. 2-stage and 3-stage separations were designed to assess achievable purities, energy costs, and area requirements. Results predict that membrane technology can be used to separate refrigerants with low energy usage to purities greater than 99.5 wt%—the purity required for refrigerant resale.

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 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 score0.278
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.229
Teacher spread0.221 · 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.

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
Published2024
Admission routes1
Has abstractyes

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