Characterization and optimization of electrodialysis with bipolar membranes with improved alkaline stability for phenol recovery from petroleum wastewater
Bibliographic record
Abstract
The purity and recovery of phenol from an aqueous solution by electrodialysis with a bipolar membrane (EDBM) system mainly depend on the solution pH, possibly effective only under alkaline conditions because phenol is a very weak acid with a pK a of 10. In this research, mono-sheet bipolar membranes with high chemical stability were successfully prepared using 1,4-diazabicyclo[2.2.2] octane (DABCO) as a quaternary ammonium group with bicyclic organic compounds. The BPMs characterization was studied using FTIR, FESEM, membrane chemical stability in alkaline solution, and electrical resistance. Comparing BPMs' performance synthesized by DABCO illustrated satisfactory results in the membrane's chemical stability and electrical resistance. The mono-sheet composite bipolar membranes are used in electrodialysis with bipolar membrane (EDBM) to remove phenol from synthetic petroleum wastewater model solution. Moreover, Response Surface Methodology (RSM) was employed as a facile method for optimizing the EDBM. In particular, the effects of current density, feed flow rate, feed concentration on the completion time (CT), and recovery efficiency (RE) of the process were investigated using the Central Composite Design (CCD) experimental design. According to the ridge and canonical analysis, the optimum operating conditions were determined at the feed concentration of 214.0 ppm, current density of 41.89, and volumetric feed flow rate of 12.84. Under these conditions, the minimum CT and maximum RE were found at 85.5 min and 75.4 %, respectively. In addition, the experimental results agreed with the prediction, suggesting that central composite design was a good technique for modeling phenol regeneration from petroleum wastewater.
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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) | 0.000 | 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 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".