A Review of Process-Design Challenges for Industrial Fermentation of Butanol from Crude Glycerol by Non-Biphasic Clostridium pasteurianum
Bibliographic record
Abstract
Butanol, produced via traditional acetone-butanol-ethanol (ABE) fermentation, suffers from low yield and productivity. In this article, a non-ABE butanol production process is reviewed. Clostridium pasteurianum has a non-biphasic metabolism, alternatively producing 1,3-propanediol (PDO)-butanol-ethanol, referred to as PBE fermentation. This review discusses the advantages of PBE fermentation with an emphasis on applications using biodiesel-derived crude glycerol, currently an inexpensive and readily available feedstock. To address the process design challenges, various strategies have been employed and are examined and reviewed; genetic engineering and mutagenesis of C. pasteurianum, characterization and pretreatment of crude glycerol and various fermentation strategies such as bioreactor design and configuration, increasing cell density and in-situ product removal. Where research deficiencies exist for PBE fermentation, the process solutions as employed for ABE fermentation are reviewed and their suitability for PBE is discussed. Each of the obstacles against high butanol production has multiple solutions, which are reviewed with the end-goal of an integrated process for continuous high level butanol production and recovery using C. pasteurianum and biodiesel-derived crude glycerol.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 0.002 |
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".