The impact of pH on VLE, pervaporation, and adsorption of butyric acid in dilute solutions
Bibliographic record
Abstract
Butyric acid (BA) is an intermediate product and a precursor to the production of butanol in ABE fermentation. Ideally, it would be beneficial to retain as much BA in the fermenter as possible to increase butanol productivity. In this study, experiments were performed to assess the impact of the pH of the feed solution on the separation of BA from dilute aqueous solutions using three separation methods: distillation, pervaporation, and adsorption. Results confirm that the pH of the solution, which dictates the level of BA dissociation, controls the degree of separation of BA from dilute aqueous solutions. Indeed, results show that the vapour‐liquid equilibrium (VLE) curve, the membrane selectivity, and the adsorption capacity for BA in dilute aqueous solutions decreased steadily as the pH is increased from below to above its pKa value of 4.82. The separation performance is strongly correlated with the pH of the feed solution, and, as anticipated, a pH increase reduces the level of separation for these three processes. This is advantageous for the ABE fermentation incorporating a solvent recovery process since BA would remain in the fermenter and improve the production of butanol. However, the pH cannot increase excessively as there exists an optimum pH for conducting the fermentation process such that a judicious level of pH must be sought to optimize a fermentation‐separation integrated process.
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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 | 0.000 |
| 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 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".