Biobutanol separation from <scp>ABE</scp> model solutions and fermentation broths using a combined adsorption–gas stripping process
Bibliographic record
Abstract
Abstract BACKGROUND Butanol is considered a promising sustainable biofuel to partly replace petroleum‐based fuels. However, to become an economically viable biofuel, some challenges need to be overcome in the biobutanol production process such as the low final product concentration caused by product toxicity to the microorganism. Few separation techniques have been proposed to extract biobutanol in situ or ex situ from dilute fermentation broths. In this investigation, the combination of gas stripping and adsorption has been studied experimentally as a process to effectively separate butanol from dilute model solutions and fermentation broths using the advantages of both separation techniques. RESULTS Results showed that the butanol adsorption capacity of activated carbon F‐400 was 261 mg g−1, for a stripped gas stream from butanol–water binary solution with an initial liquid phase composition of 15 g L−1 butanol, which ended up having a vapour phase composition of 5.8 mg L−1 after gas stripping. This capacity is relatively high compared with the values reported in the literature. Butanol adsorption capacities for a stripped gas stream in equilibrium with ABE model solutions (5.1 mg L−1) and fermentation broths (2.3 mg L−1) for this adsorbent (211.6 and 219.8 mg g−1, respectively), were also higher than the capacities reported in the literature. CONCLUSION Combined gas stripping and adsorption could be considered an effective technique for biobutanol separation processes. © 2016 Society of Chemical Industry
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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) | 0.001 | 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 | 0.000 |
| 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".