Algal biomass dual roles in phycoremediation of wastewater and production of bioenergy and value-added products
Bibliographic record
Abstract
Abstract Algal biomass has been gaining attention over the last decades as it is versatile and can be used in different industries, such as wastewater treatment and bioenergy industries. Microalgae are mixotrophic microorganisms that have potential to utilize nitrogen and phosphate (nutrients) and remove organic matters from wastewater streams. Phycoremediation is an intriguing and cost-efficient technique to simultaneously remove heavy metals from wastewater while removing nutrients and organic matters. The cultivated and produced algal biomass can be a promising candidate and a sustainable feedstock to produce biofuels (e.g., biodiesel, bio-alcohol, and bio-oil) and value-added products such as biochar, glycerol, functional food, and pigments. The algae suspended cultivation systems, WSP and HRAP, are efficient methods for the wastewater treatment in shallow ponds with no mechanical aeration and less required energy consumption, but when a short HRT and minimum evaporation losses are key points in the algal cultivation the PBRs are recommended. It was reported that biosorption and bioaccumulation are the two promising techniques of phycoremediation. Studies showed that among the current processes of algal biomass conversion to biofuels, transesterification of algal lipids and pyrolysis of algal biomass were found to be the most efficient techniques. This review paper investigates the applications of algal biomass in the phycoremediation of wastewater, productions of bioenergy and value-added products by reviewing articles mainly published over the last five years. Graphical abstract
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".