KINETIC MODEL DEVELOPMENT ON SOURCE-SEPARATED ORGANIC WASTE FOR ETHANOL PRODUCTION BY S. cerevisiae STRAIN
Bibliographic record
Abstract
Ethanol production from organic fraction of municipal solid waste with inclusion of construction/demolition waste can be an effective waste management strategy to overcome the growing problems with landfill space and dependency on conventional fuels.The main challenge in ethanol conversion is the high cost of processing in which pre-treatment, enzymatic hydrolysis and fermentation are the major steps.This study investigates impact of several key parameters, namely: pH, temperature, adsorption capacity, cellulose hydrolysis rate, cell mass, enzyme and substrate loading doses on ethanol yield.The pre-treatment incorporates pre-processing and enzymatic hydrolysis steps through the use of a thermal screw press (TSP) and cellulose-organic-solvent based lignocellulosic fractionation (COSLIF) on the source-separated organic (SSO) waste to liberate fermentable sugars.Enzymatic hydrolysis experiments were featured with the addition of a commercially available enzyme complex, Accellerase 1500, to mediate the process and increase sugar yields.A kinetic model that uses a semi-mechanistic rate equation for cellulose hydrolysis was adapted and modified to accommodate batch simultaneous saccharification and co-fermentation (SSCF) process on pre-treated SSO waste by yeast, Saccharomyces cerevisiae DA2416.New experimentally defined SSO parameters have been fitted into a kinetic model to evaluate the sugar and ethanol yields.It was found that the model was capable of predicting ethanol productions with diminutive variance from experiments with substrate concentrations between 10 g/L and 50 g/L.Model predictions from experimental data deviated significantly with substrate loading rate from 60 g/L and higher.Fermentation results demonstrated that S. cerevisiae DA2416 produced ethanol in the range of 35 -50 g/L, with ethanol yield of 0.48 -0.50 g of ethanol/g sugar, in 5 days with 96% cellulose conversion.This study provides important insights for investigation on the use of SSO waste for ethanol production by S. cerevisiae DA2416.Furthermore, the model was proven to be a useful tool to facilitate future process optimization for up-scale bioreactors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".