Sustainable microalgae‐based palm oil mill effluent treatment process with simultaneous biomass production
Bibliographic record
Abstract
The production of copious quantities of waste palm oil mill effluent (POME) is an unavoidable consequence of palm oil industries, and requires effective treatment before discharge into the environment. Microalgae possess a significant nutrient bio‐sorption capacity in addition to high photosynthetic and carbon bio‐sequestration rates, and hence can be exploited for sustainable POME treatment operations. Bioprocess research on the use of microalgal cells to remove specific chemical species from POME is limited. This work investigated the application of the microalgae Chlorella vulgaris and Nannochloropsis sp. for nitrogen and phosphorus removal from POME with simultaneous biomass production. Both microalgae species displayed maximum total nitrogen and phosphorus removal efficiencies at 50 % POME concentration within 8 days. Studies on nitrogen and phosphorous addition demonstrated that a N/P molar ratio of 10:1 improved biomass accumulation with 90.0 % nitrogen and 82.1 % phosphorus removals. These results showed that high treatment efficiencies can be obtained using C. vulgaris and Nannochloropsis sp. for applications in industrial POME treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".