Transitioning Towards a Circular Economy in Québec: An Integrated Process for First-, Second- and Third-Generation Ethanol from Sweet Sorghum and <i>Chlorella vulgaris</i> Biomass
Bibliographic record
Abstract
Full feedstock potential needs to be tapped to make lignocellulosic ethanol an economically viable reality. This work focuses on the Saccharomyces cerevisiae ethanol fermentation of fresh sorghum carbohydrates extracted through a mild steam-treatment process, and the subsequent Chlorella vulgaris cultivation using the generated liquid and gaseous fermentation effluents. The first section of the manuscript focuses on the effect of nutrient addition (fermentation effluent, yeast extract and urea) on the conversion efficiency of the sorghum carbohydrates to ethanol. Overall, the fermentation time was reduced to half when yeast extract and urea were supplemented to the free and hemicellulosic carbohydrate stream, accelerating the total sugar consumption time from 24 h to under 12 h. However, regarding the cellulosic carbohydrate hydrolysate, the sole addition of urea resulted in a slight improvement of the fermentation kinetics. The second half of the manuscript presents the impact of these different fermentation effluents and various process parameters (addition of yeast extract, antibiotic and CO2) on the microalgal cultivation and composition. The cellulosic hydrolysate yielded the highest concentrations of microalgal carbohydrates (507 mg/L) under a CO2-rich environment. Further cultivation scale-up assays confirmed these observations in the presence of 10% CO2 using the mixed fermentation effluents of the free and constitutive sorghum carbohydrates. Thus, an integrated sorghum-based first- (free carbohydrates), second- (constitutive carbohydrates) and third-generation (microalgal carbohydrates) ethanol production process was thoroughly investigated. This work could represent a step towards bridging the gap leading to full-scale commercialization of these advanced-biofuel technologies.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".