Phycoremediation and simultaneous production of protein‐rich algal biomass from aquaculture and agriculture wastewaters
Bibliographic record
Abstract
Abstract BACKGROUND Microalgae cultivation in wastewaters is a potential remediation technology for converting residual nutrients into valuable biomass for biofuels, fertilizers, animal and aquaculture feeds, and bio‐based chemicals. The objective of this study was to determine the potential for microalgae phycoremediation of aquaculture and agriculture wastewaters for sustainable production of nutrient‐rich algal biomass. RESULTS Thirty‐seven aquaculture and agriculture wastewaters were evaluated as growth media for two freshwater, chlorophytic microalgae strains (Chlorella sorokiniana SMC14M and Scenedesmus sp. AMDD). Although nutrient levels and physicochemical parameters of the wastewaters varied widely, phycoremediation efficiency of nitrogen and phosphorous was high (> 70%) for a large majority of them, with both microalgae strains. Photobioreactor cultivation (300 L) of C. sorokiniana grown on finfish aquaculture wastewater (CA2) and standard growth medium (SGM) exhibited similar biomass production levels and nutritional compositions of moisture (4.9–5.0%), ash (5.9–6.1%), nitrogen (8.6–8.8%), protein (41.0–42.1%), lipid (8.6–8.8%), carbohydrate (38.3–39.3%) and gross energy (22.2–22.3 MJ⋅kg−1) with highly similar fatty acid and amino acid profiles. CONCLUSIONS This study demonstrates that the use of freshwater microalgae to remediate various aquaculture and agriculture wastewaters, while producing valuable nutrient‐rich biomass, is feasible. We have found that a particular finfish aquaculture wastewater (CA2) contains all of the nutrients required for rapid microalgae growth, avoiding the need for expensive commercial fertilizers. The biochemical composition of C. sorokiniana grown on CA2 aquaculture wastewater has good potential for use as a nutrient‐rich ingredient for animal and aquaculture feeds, fertilizers products, biofuels and other applications, resulting in remediated water that could be reused. © 2023 National Research Council Canada. Reproduced with the permission of the Minister of Innovation, Science, and Economic Development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".