Bioreactors for Natural Gas Desulfurization
Bibliographic record
Abstract
Abstract Approximately half of the reserve gas in the U.S. is subquality, meaning it contains contaminants such as hydrogen sulfide (H2S) or carbon dioxide. One of the most common problems in the gas industry is the removal and disposal of H2S also known as natural gas desulfurization or sweetening of sour gas. Many traditional methods of H2S removal are costly, energy intensive, and potentially dangerous. Therefore, these methods may not be suitable for some gas-production sites. Biological oxidation offers a safe, energy efficient, and cost-effective method for natural gas desulfurization. This laboratory has investigated biological oxidation of H2S for some time and the current research focuses on bioreactors to treat ‘stranded’ natural gas. Preliminary data were previously presented at EPEC 20021; this paper includes new designs and experimental data. Bioreactors utilizing the immobilization matrices Bio-Sep® and Bio-Sep®S (sufide-sorbing version) inoculated with Thiobacillus denitrificans have been used to treat a gas stream containing 10,000 ppm H2S. The desired removal efficiency is greater than 99%. Stirred-tank reactors were operated with both matrices for 3 and 8 months at maximum gas flow rates of 130 and 72 mg H2S/h for the original and sulfide-sorbing beads respectively, with removal efficiencies near 99.9%. Phospholipid fatty acid (PLFA) analysis revealed cell densities on the order of 1010 cells/g of bead. Packed-bed reactors will also be operated with both matrices. Feasibility studies will be conducted for both types of reactors. This research is ongoing with the goal of developing an economical bioreactor to treat ‘stranded’ natural gas.
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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 | 0.001 |
| 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".