Successful conversion of corn stover into microbial lipids at high solids loading by Rhodosporidium toruloides at pilot scale
Bibliographic record
Abstract
Lignocellulosic biomass (LCB) offers potential feedstocks for biofuels. As it generally involves processes including pretreatment, hydrolysis, and fermentation, LCB-based biorefinery at higher solids loading may improve substrate concentration and reduce operational costs, yet its scaling-up remains a challenge. Here, efficient conversion of corn stover (CS) at high solids loading into microbial lipids was demonstrated in a 1 000 L pilot-scale bioreactor using Rhodosporidium toruloides CGMCC 2.1389. The process employed lab-optimized conditions, including alkaline storage pretreatment of CS (AS-CS) with 6% NaOH for over 60 d at room temperature. The AS-CS (100 kg) was steamed in the 1000 L bioreactor at 121 °C for 1 h at a solids loading of 20%, followed by subsequent removal of alkaline black liquor (ABL) using squeeze technology. The leftover residues were hydrolysed by enzymes with a total reducing sugar (TRS) recovery of 93.6%. The lipid production in the 1 000 L bioreactor resulted in a lipid titer of 10.6 g/L and a yield of 0.194 g/g (based on consumed TRS). The mass flow analysis suggested that 89.6% of cellulose and 95.5% of hemicelluloses were released to produce lipids with little lignin by-products, avoiding their toxic effects on lipid production. The developed process in this work offers a promising avenue for industrial conversion of LCB into microbial lipids.
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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) | 0.000 | 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 | 0.001 |
| 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 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".