An innovative integrated system developed for hydrogen fuel production and sulfur recovery from acid gas streams for sustainable development
Bibliographic record
Abstract
This study presents the development of a novel integrated energy system for clean hydrogen production and sulfur recovery from waste acid gas streams. The process involves rectification of the acid gas to obtain pure hydrogen sulfide (H 2 S), which serves as the primary feedstock. A specific filtration combustion technology is incorporated into the developed system to process H 2 S, producing H 2 and simultaneously recovering sulfur. This approach provides an efficient solution for acid gas utilization and desulfurization. A comprehensive thermodynamic analysis is employed to study energy and exergy efficiencies for the overall system and its subsections. The system's mass and energy balances are simulated using the Aspen Plus software. Many parametric studies are conducted to scrutinize the impact of key operating variables on H 2 production and S recovery. The study expands the scope of analysis by investigating a comprehensive range of feed additive scenarios in the filtration combustion of H₂S, including enriched air, Argon, CO₂, SO₂, steam, and unreacted H₂S, to optimize H₂ production and sulfur recovery. With an H 2 S feed rate of 0.146 kg/s, the system achieves a hydrogen production rate of 0.00183 kg/s and a sulfur recovery rate of 0.0112 kg/s. Parametric study results show that the base-case scenario results in an optimal sulfur recovery rate, while the steam additive scenario increases H 2 production by 4.23% compared to the baseline. The integrated system demonstrates high thermodynamic performance, with overall energy and exergy efficiencies of 91.73% and 75.33%, respectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".