Application of Response Surface Methodology (RSM) For Optimization of Hydrogen Sulphide Adsorption Using Coconut Shell Activated Carbon Xerogel: Effect of Adsorption Pressure and Hydrogen Sulphide Flowrate
Bibliographic record
Abstract
To improve the adsorption of hydrogen sulfide (H2S) by using coconut shell-activated carbon xerogel (CSACX), we adopted the response surface methodology (RSM) with a central composite design (CCD). This material was created by incorporating a crosslinker agent, initiator agent, and polymer. The process of creating CSACX involved synthesizing coconut shell activated carbon into a wet gel using chemicals such as sodium alginate, calcium carbonate, glucono delta-lactone (GDL), and distilled water in a sol-gel method to obtain a xerogel. Afterward, the gel was dried in an oven at 60 for 24 hours. Subsequently, it was used as an adsorbent for the adsorption test. The adsorption test was conducted at two different initial concentrations of H2S, 25 ppm, and 50 ppm, to assess the effectiveness of H2S removal at different concentrations. In the RSM approach, we selected adsorption pressure (1-3 bar) and H2S flow rate (100-300 L/hr) as the process variables while maintaining a constant contact time (5 minutes), adsorbent weight (11 g) and temperature (30). The removal efficiency of H2S (%) was chosen as the response. Our findings showed that the optimum conditions for H2S removal were at 1 bar and 100 L/hr for 25 ppm of H2S and 1 bar and 100.3830 L/hr for 50 ppm of H2S. The model generated from RSM predicted that maximum H2S removal can be achieved at a lower pressure and flow rate for any H2S initial concentration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".