Integrated Microalgae Cultivation for Sustainable Wastewater Treatment and Carbon Dioxide Bio-Fixation in Milk Factories
Bibliographic record
Abstract
Technologies have been developed to reduce CO2 emissions, including CO2 bio-fixation by microalgae.This study evaluates carbon reduction by integrating milk factory wastewater treatment with microalgal biomass production, including its sustainability aspects through life cycle assessment (LCA) and techno-economic assessment (TEA).The microalgae species used were a consortium of Chlorella sp. and Scenedesmus sp.Five levels of carbon dioxide were provided to microalgae cultures: 0%, 5.5%, 6.2%, 8.1%, and 10.3%.Observed variables included CO2 uptake, absorption efficiency, and microalgal biomass production.The results showed that the CO2 sequestration efficiency by indigenous microalgae reached 0%, 9.2%, 98.8%, 96.2%, and 93.2% with average CO2 level loadings of 0%, 5.2%, 6.2%, 8.1%, and 10.3%, respectively.Chlorella sp.exhibited greater tolerance to high levels of CO2 concentration than Scenedesmus sp.TEA analysis revealed that CO2 bio-fixation and wastewater utilization significantly increased microalgal biomass production while also reducing environmental pollution.Furthermore, LCA indicated that the initial biomass production method (scenarios 1 and 2) had a higher environmental impact than the advanced method using wastewater treatment (scenarios 3 and 4).In conclusion, coupling microalgaebased wastewater treatment with CO2 bio-fixation offers promise for CO2 mitigation, enhanced biomass production, and reduced operational costs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".