Ethanol Recovery from Fermentation Broth via Carbon Dioxide Stripping and Adsorption
Bibliographic record
Abstract
With the depletion of fossil fuels, research in alternative energy sources that can provide the world’s energy demand has increased significantly during the past decade. One alternative energy source in particular that has gained worldwide recognition as a potential replacement to oil is bioethanol. It is renewable and environmentally friendlier than fossil fuels. In this study, adsorption is used to increase the efficiency of ethanol production by decreasing the effect of product inhibition using carbon dioxide stripping technology. The reduction in product inhibition is particularly important when ethanol is produced from lignocellulosic biomass because microorganisms that are able to use all fermentable sugars are less tolerant to ethanol. Carbon dioxide removes ethanol from the fermentation broth and reduces the level of ethanol toxicity, while adsorption is used to recover the entrained ethanol from the vapor phase. The literature review showed that activated carbon and hydrophobic zeolites would be the most appropriate adsorbents for ethanol recovery in the vapor phase. A series of adsorption screening experiments were performed to compare four activated carbon adsorbents (Filtrasorb 200, Nuchar RGC 40, Sorbonorit B4, and WV-B 1500) and two hydrophobic ZSM-5-type zeolites (HiSiv 3000 and CBV 8014). The vapor composition used was controlled to resemble the fermenter outlet vapor concentration after stripping with carbon dioxide. Activated WV-B 1500 exhibited the highest ethanol capacity among activated carbons, having higher ethanol adsorption capacities than the two zeolites. Adsorption isotherms for ethanol and water in the presence of carbon dioxide at different temperatures were determined using WV-B 1500 as the adsorbent with the temperature-dependent Toth isotherm model providing satisfactory fits for these isotherms. Ethanol adsorption experiments with and without the presence of water were conducted and showed similar ethanol adsorption capacities, indicating that the presence of water has a negligible effect on ethanol adsorption.
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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".