Integrated lignocellulosic biorefinery: Gateway for production of second generation ethanol and value added products
Bibliographic record
Abstract
An increasing demand for energy and depleting petroleum sources has elevated the need for producing alternative renewable resources. Owing to the prominence of lignocellulosic biomass as bio-renewable and the most abundant resource on Earth, this critical review provides perceptions into the potential of lignocellulosic biomass for production of second generation (2G) ethanol and value added products in a biorefinery manner. The efficient utilization of all three components of lignocellulosic biomass (i.e., cellulose, hemicellulose and lignin) would play a significant role in the economic viability of cellulosic ethanol. The pretreatment method is the key to the success of bioconversion processes and greatly influences the economics of biorefinery process. Biotechnology tools and process engineering play pivotal roles in development of integrated processes for production of biofuels, biochemicals and biomaterials from lignocellulosic biomass. Although, lignocellulosic biorefinery has ample scopes, commercial production of biofuels and chemicals is still challenging. In this context, this review entails concept of lignocellulose biorefinery, latest developments in 2G ethanol production process, importance and market potential of 2G ethanol as renewable fuel and value added chemicals, integration of processes, challenges for integrated production of fuel together with value added chemicals and future directions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".