Enhancing Flare Gas Treatment: A Systematic Evaluation of Dual-Stage (Amine, CO2 Supercritical) and Hybrid Approaches Using HYSYS
Bibliographic record
Abstract
The flaring of associated gas in oil and gas operations contributes significantly to greenhouse gas emissions and represents a loss of valuable hydrocarbon resources. While amine absorption is widely applied for acid gas removal, the use of supercritical carbon dioxide (sc-CO2) for flare gas treatment remains largely unexplored, despite its proven selectivity for hydrocarbons in other industries such as natural product extraction and polymer processing. Conventional flare gas treatment methods face trade-offs: amine absorption achieves high acid gas removal efficiency but offers limited selectivity for heavier hydrocarbons, whereas sc-CO2 extraction enables efficient recovery of higher hydrocarbons but does not fully remove acid gases. This study addresses these gaps by evaluating three two-stage flare gas treatment configurations—dual-stage amine absorption, dual-stage sc-CO2 absorption, and a hybrid of sc-CO2 followed by amine absorption—using Aspen HYSYS V12.1 simulations, with recycling processes considered in each case. The dual-stage sc-CO2 process achieved nearly complete hydrocarbon recovery (100%) and complete H2S removal, but CO2 remained at elevated concentrations in the treated gas. The dual-stage amine process completely removed CO2 and H2S, though with higher energy demand for solvent regeneration. The hybrid configuration combined the advantages of both approaches, achieving complete H2S removal, 100% hexane recovery, 95.02% methane recovery, and a drastic reduction in CO2 concentration (to 0.0012 mole fraction). These results demonstrate that integrating sc-CO2 with amine absorption resolves the trade-off between hydrocarbon selectivity and acid gas removal, establishing a technically viable pathway for flare gas utilization with potential application in gas-to-liquids (GTL) and carbon management strategies
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".