A Novel Approach to Hydrogen Sulfide Removal From Natural Gas
Bibliographic record
Abstract
Abstract Sulfide removal is a significant problem in the oil and gas industry. Some of the problems presented by sulfides include environmental compliance, toxicity, corrosion, reduced efficiency of fluid handling equipment, offensive odor, reduced value of products, and increased operation costs. Microbial oxidation of sulfides offers the potential for a safe, cost-effective method of removing sulfides from natural gas, sour water, spent-sulfidic caustic, etc. The purpose of this study is the development of an efficient and economically viable bioreactor system for sulfide oxidation. The application of immediate interest is the removal of H2S from "stranded" natural gas. The immobilization matrix Bio-Sep® has previously been used for successful biotreatment of BTEX-contaminated groundwater. In this study, a special sorbent has been added to Bio-Sep® to adsorb sulfide while still maintaining the desirable physical properties of the original beads. Thiobacillus denitrificans is a sulfide-oxidizing autotroph which may use either oxygen or nitrate as a terminal electron acceptor. Thiosulfate, elemental sulfur, or sulfide may be used as an energy source for T. denitrificans and each are oxidized to sulfate. Previously, suspended cultures of T. denitrificans were shown to remove H2S from a gas stream with 1-2 s of gas-liquid contact time. However, the volumetric productivity of suspended cultures was insufficient to lead to an economically viable bioreactor design due to low biomass concentrations. In order to increase biomass concentration and volumetric productivity T. denitrificans has been immobilized in both standard Bio-Sep® and the new sulfide-sorbing version (Bio-Sep®S). The two matrices will be compared according to their ability to immobilize this sulfide-oxidizing culture and increase biomass concentration and volumetric productivity.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".