Effects of Oscillating Injection Conditions of CO<sub>2</sub> onto its Adsorption Performance within a Packed-Bed Reactor
Bibliographic record
Abstract
Carbon capture and storage (CCS) through adsorption onto activated carbon in packed-bed reactors is crucial for mitigating CO emissions.Optimizing these reactors' efficiency requires comprehensive exploration of various operational conditions.In this study, we investigated the influence of oscillating injection conditions, specifically periodic variations in temperature and pressure, on CO adsorption performance and associated energy efficiency.To achieve this, we employed a numerical model combining Computational Fluid Dynamics (CFD) with a Linear Driving Force (LDF) adsorption approach, enabling detailed simulation of heat and mass transfer within the reactor.Initially, sinusoidal and triangular waveforms were employed to evaluate the impact of periodic injection conditions on the adsorption dynamics.The results confirm that pressure fluctuations significantly affect adsorption performance while temperature fluctuations show negligible impact, highlighting pressure as the dominant injection parameter.Subsequently, the core analysis examined stepwise injection scenarios with variable holding times at high and low pressures to simulate practical industrial operating conditions.Results indicate that shorter holding times at high pressures reduce the overall adsorption efficiency due to insufficient contact time, while extended durations at elevated pressures significantly enhance CO uptake despite increased compression energy demands.Energy consumption analyses, incorporating compression and cooling metrics, demonstrated clear trade-offs between energy efficiency and adsorption performance under fluctuating conditions.Ultimately, our findings highlight that optimizing holding durations at elevated pressures in stepwise injection scenarios can substantially improve CO capture performance, offering crucial insights for the design of industrial adsorption systems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".