Cultivation of Microalgae in Municipal Wastewater and Conversion by Hydrothermal Carbonization: A Review
Bibliographic record
Abstract
Abstract The idea of growing microalgae in wastewaters emerges from the idea of resource conservation and the recovery of nutrients. In fact, microalgae are able to take up nitrogen, phosphorus and carbon from wastewaters, even adsorb metals, and in many cases, can be co‐cultivated with various bacteria that are prevailing in municipal wastewater treatment plants. The cultivation of microalgae in municipal wastewater has been known for about half a century and investigated accordingly. Despite this long history, there are still many questions to answer before this technology will be ready for implementation in large‐scale projects. In this review, recent developments are presented. One crucial point in developing a viable process out of wastewater grown algae is the downstream processing of the accumulated algal biomass. The authors decided to focus on hydrothermal carbonization (HTC) as a processing strategy. HTC uses wet biomass and relatively mild process conditions to produce an energy‐rich biochar and a liquid fraction that can be further processed to higher‐value substances. The latest findings in the carbonization of microalgae are highlighted in the second part of this 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".