Simulation study of deep eutectic solvent-based biogas upgrading process integrated with single mixed refrigerant biomethane liquefaction
Bibliographic record
Abstract
Deep eutectic solvents (DESs) comprise ChCl/urea, in combination with water, have been considered in removing acid gases (CO2 and H2S) from biogas. The evaluation of DES for biogas upgrading at relatively high pressure (i.e., >8.0 bar) has not been reported before. The aqueous DES performance has also not been analyzed compared to conventional amines-based solvent (MEA) and ionic liquid (IL). To the best of our knowledge, this is the first study that presents the integration of DES-based biogas upgrading with a mixed refrigerant liquefaction process to facilitate the safe and economical transportation of biomethane over long distances. The biogas considered in this study consisted of 60% CH4, 39% CO2, and 1% H2S. The aqueous ChCl/urea (70 wt%) results in biomethane with ≥99.0 wt% purity and ≥97.0 wt% recovery. Then, this biomethane was liquefied with ≥90% liquefaction rate. Based on the results obtained herein, overall capital, operating, and total annualized cost savings of 2.8%, 25.82%, and 14.26% were achieved using the 70% DES-based integrated process in comparison with the MEA-based integrated process. Whereas 1.41%, 16.85%, and 8.71% capital, operating, and total annualized costs could be saved in comparison with the IL (i.e., [Bmim][PF6])-based integrated process. It could be deduced that the overall cost of the biomethane value chain can be reduced using the proposed approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".