Bioconversion process of source-separated organic waste for ethanol production
Bibliographic record
Abstract
Production of biofuel such as ethanol from lignocellulosic biomass is a beneficial way to meet sustainability, energy security, and environmental goals. Lignocellulosic biomass such as source-separated organic (SSO) waste is particularly attractive since it is widely available, often at a negative cost, reduce the land depletion from using food-based biomass for ethanol production and reduce the amount of generated waste. Therefore, in order to meet the future fuel demands and cope with increasing volume of municipal waste this study was a first attempt to use SSO as a feedstock for ethanol production. The main objectives of the study were: a) to compare standard and modified celluloseorganic- solvent-based lignocellulosic fractionation (COSLIF) pretreatment of SSO waste for ethanol production in terms of enzyme savings, sugar formation and ethanol yields; b) to produce ethanol from SSO by using modified COSLIF pretreatment and fermentation with two different recombinant strains: Z. mobilis 8b and S. cerevisiae DA2416; and c) to develop experimental kinetic model capable of predicting behavior of batch SSCF on SSO waste with different SSO substrate concentrations using Berkeley Madonna program. Based on the obtained results, it was found that SSO is an excellent feedstock material for ethanol conversion. The efficiency of modified COSLIF pretreatment was improved by 20% compared to standard method using ethanol washing of pretreated SSO samples during the experimental procedures instead of acetone. On average, glucose yield from SSO samples pretreated by modified COSLIF was about 90% compared to 10% for untreated samples. S. cerevisiae DA2416 outperformed Z. mobilis 8b on ethanol yields during the fermentation process, with 0.50 g ethanol/g potential sugar fed on SSO in less than 5 days, with a 96% cellulose conversion, totalling in 150 g/L ethanol produced. A kinetic model with newly integrated values of experimentally defined SSO feedstock constants was proven to predict the ethanol yield accurately with substrate concentration ranges of 20 g/L - 50 g/L. Model prediction at higher substrate concentration (e.g. 100 g/L) deviated from the experimental values, suggesting that ethanol inhibition is a major factor in bioethanol conversion.
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 | 0.000 | 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 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".