Drying of nonpolar gas in a pressure swing adsorption process using canola meal biosorbents
Bibliographic record
Abstract
Abstract Drying of gases is a very important industrial operation. In the present work, drying of nonpolar gas was carried out using nitrogen as a model gas, by selective adsorption of water vapor from the moist gas using canola meal (CM)‐based biosorbents in a pressure swing adsorption process. It was demonstrated that CM did not adsorb nonpolar nitrogen but selectively adsorbed polar water vapor. Five operating parameters—temperature, pressure, input feed water concentration, input gas concentration, and particle size—were chosen to study the nitrogen drying process using a fractional factorial design. Temperature and input water concentration had significant effects on the drying process, and the maximum water adsorption capacity obtained was 0.165 kg/kg.ads. The Dubinin–Polanyi (DP) model for large pores fit the water adsorption isotherms reasonably well and indicated that water adsorption is predominantly physisorption. Furthermore, site energy distribution of water adsorption based on the DP model was carried out to determine adsorbate–adsorbent interactions. It revealed that most of the adsorption sites were in the low‐energy region of the distribution (>7,800 J/mol) and there were negligible sites with energy higher than 25,000 J/mol which again confirms that water adsorption is rapid, reversible, and low‐energy process, which is the characteristic of physisorption. The average energy and standard deviation of the site energy distribution was 5,000 J/mol. Saturated biosorbent was regenerated at 110°C and reused multiple times. The analysis of the present work can be applied to similar systems for drying of nonpolar gases.
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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 | 0.000 |
| 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".