Algae to Biofuels: Catalytic Strategies and Sustainable Technologies for Green Energy Conversion
Bibliographic record
Abstract
The global population surge and continuously rising energy demand have led to the rapid depletion of fossil fuel reserves. Over-exploitation of non-renewable fuels is responsible for the emission of greenhouse gases, air pollution, and global warming, which causes serious health issues and ecological imbalance. The present study focuses on the potential of algae-based biofuel as an alternative energy source for fossil fuels. Algal biofuels are more environmentally friendly and economically reasonable to produce on a pilot scale compared to lignocellulosic-derived biofuels. Algae can be cultivated in closed, open, and hybrid photobioreactors. Notably, high-rate raceway ponds with the ability to recycle nutrients can reduce freshwater consumption by 60% compared to closed systems. The algal strain along with various factors such as light, temperature, nutrients, carbon dioxide, and pH is responsible for the growth of biomass and biofuel production. Algal biomass conversion through hydrothermal liquefaction (HTL) can achieve higher energy return on investments (EROI) than conventional techniques, making it a promising Technology Readiness Level (TRL) 5–6 pathway toward circular biorefineries. Therefore, algal-based biofuel production offers numerous benefits in terms of socio-economic growth. This review highlights the basic cultivation, dewatering, and processing of algae to produce biofuels using various methods. A simplified multicriteria evaluation strategy was used to compare various catalytic processes based on multiple performance indicators. We also conferred various advantages of an integrated biorefinery system and current technological advancements for algal biofuel production. In addition to this, policies and market regulations are discussed briefly. At the end, critical challenges and future perspectives of algal biorefineries are reviewed. Algal biofuels are environmentally friendly as well as economically sustainable and usually offer more benefits compared to fossil fuels.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".