Ethanol production by syngas fermentation in a continuous stirred tank bioreactor using<i>Clostridium ljungdahlii</i>
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Bibliographic record
Abstract
Ontario biomass could be thermochemically processed by dry and wet torrefaction to produce high quality solid biofuel. These solid fuels or raw biomass could also be gasified to produce syngas. This study analyzes and demonstrates a successful and efficient way of producing bioethanol from syngas fermentation using Clostridium ljungdahlii in a laboratory scale continuous stirred tank bioreactor having an innovative gas supply and effluent extraction structures. At the beginning of the experiment, a batch process was conducted to grow microorganisms and allow the growth of the microorganisms to reach to maximum cell density in a reactor without supplying a gas. Ethanol production was observed by supplying two different gas compositions which included 100% CO and simulated syngas, mimicking the composition of syngas extracted from lignocellulosic biomass having 60% CO, 35% H2, and 5% CO2. CO and syngas were fermented with different gas flow (5–15 mL/min), effluent flow (0.25–0.75 mL/min), and media flow rates and stirrer speed (300–500 rpm) at atmospheric pressure and 37˚C. The gas flow rate, media and effluent flow rate, pH level, and stirrer speed were controlled during the fermentation process. The exhaust gas was reused for the improvement of residence time using a loop-back system for improving the gas–liquid mass transfer. Excessive foam was observed during the fermentation process which was controlled using diluted antifoam-204. Maximum cell concentration reached 2.4 g/L. The mass transfer coefficient showed better performance during syngas fermentation than CO fermentation. More bioethanol production was observed by syngas fermentation than CO fermentation. CO fermentation produced 0.17–1.33 g/L-effluent ethanol and 8.92–23.67 g/L-effluent acetic acid whereas syngas fermentation produced 0.85–3.75 g/L-effluent ethanol and 8.89–14.97 g/L-effluent acetic acid.
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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 it