CO2 Capture by Dual Hollow Fiber Membrane Systems
Bibliographic record
Abstract
In this paper, a system for efficient removal of carbon dioxide by hollow fiber membranes is proposed. The system is compact, and it is very useful for application in the offshore energy industries. In particular, it is used to removing CO<sub>2</sub> from the exhaust of power generation facilities on offshore platforms.<br />The proposed dual membrane contactor contains two types of membranes (polypropylene membrane and silicone rubber membrane); moreover, the dual membrane contactor was designed and constructed for gas absorption processes. The module performance was evaluated based on permeation flux experiments. The experimental results were compared with the predictions from a numerical model developed in our previous studies. Furthermore, the mass transfer resistance in the fabricated module was investigated using resistance-in-series model. It is proposed that the computational techniques be used to develop design techniques in these kinds of complex systems. In addition, experimental methodologies have been used for the design and optimization of cross-flow hollow fiber membrane modules to absorb or desorb the gas. However, the experiments can be expensive and time consuming.<br />Numerical simulations used in conjunction with experiments can decrease the number of required experiments, thus reduce the required costs and time. In this work, a new modelling approach using computational fluid dynamics (CFD) is proposed to improve modelling flow within cross-flow membrane modules, and subsequently as a design means for such modules. In the CFD model, the fiber bundle is modeled as a porous medium to capture flow characteristics through the fiber bundle.<br />Also, mass transfer equations in the fiber and shell sides are coupled and solved using an iteration algorithm by taking consideration of the influence of flow behavior of both gas phase and liquid phase. In parallel, experimental study was also carried out to validate the results of computational modeling.<br />The CFD modeling results correlated well with the experimental data obtained from a lab scale cross-flow membrane module with uniform distributed fibers. The developed model was then used to examine the performance of modules with more complex geometries such as baffled modules and modules containing unevenly distributed fiber bundles. Finally, it was demonstrated that the CFD simulation is a promising approach in developing and optimizing cross-flow membrane module.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".