A Review of Liquid and Gaseous Biofuels from Advanced Microbial Fermentation Processes
Bibliographic record
Abstract
Biofuels are the sustainable counterparts of fossil fuels to meet the increasing energy demands of the current and future generations. Biofuels are produced from waste organic residues with the application of mechanical, thermochemical and biological methods and processes. While mechanical and thermochemical conversion processes involve the use of heat, pressure, catalysts and other physicochemical attributes for the direct conversion of biomass, biological conversion requires microorganisms and their enzymes as biocatalysts to degrade the fermentable substrates into biofuels and biochemicals. This article highlights the advances and opportunities in biological conversion technologies for the development of a closed-loop biorefinery approach. This review highlights the distinction between biological and thermochemical conversion technologies, including a discussion on the pros and cons of the pathways. Different categories of biological conversion processes, such as enzymatic saccharification, submerged fermentation, solid-state fermentation and simultaneous saccharification and fermentation are also discussed in this article. The main essence of this article is the description of different fermentative technologies to produce next-generation biofuels, such as bioethanol, biobutanol, biomethane, biohydrogen and biodiesel. This article provides a state-of-the-art review of the literature and a technical perspective on the bioproduction of bioethanol, acetone–ethanol–butanol fermentation, anaerobic digestion, photo/dark fermentation, and the transesterification of lignocellulosic substrates to produce the above-mentioned biofuels. In addition, recommendations for improving bioprocessing efficiency and biofuel yields are provided in this comprehensive article.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".