Adsorptive separation of propionic acid from aqueous medium using efficient anion exchange resins in batch and fixed bed column systems
Bibliographic record
Abstract
Abstract The recovery of propionic acid (PA) from aqueous medium using a strongly basic, Lewatit M‐600 (LM‐600), and a weakly basic, Lewatit MP‐62 (LMP‐62), anion exchanger was investigated. Medium conditions such as pH, temperature, PA concentration, and resin dose affected the performance of the process. The highest capacities were achieved at pH 5 and 2 with LM‐600 and LMP‐62, respectively. Equilibrium was reached in 35 min, and the process followed the pseudo‐second‐order kinetics for both anion exchangers. LMP‐62 was more effective at pH values lower than 5. The exothermic process was found to be spontaneous. The maximum capacities of the strong and weak basic exchangers in batch experiments were 135.7 and 363 mg/g, respectively. However, the breakthrough capacities obtained in fixed bed systems were lower than those attained in batch systems (94.75 and 123.73 mg/g for LM‐600 and LMP‐62, respectively), and the column was successfully regenerated using 0.6 M NaOH. The Freundlich isotherm model yielded the highest determination coefficients in the isotherm analysis, suggesting that physical forces between PA and resins primarily drive the process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".