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Record W2969389571 · doi:10.1504/ijgw.2019.10023359

Investigation of lipid, carbohydrate and protein production from <i>Chlorella vulgaris</i> in controlled environment minkery wastewater

2019· article· en· W2969389571 on OpenAlexaff
İlhami Yıldız, Yuchen Ji

Bibliographic record

VenueInternational Journal of Global Warming · 2019
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPhotobioreactorChlorella vulgarisWastewaterMixotrophAnimal scienceFood scienceLight CycleChlorellaLight intensityChemistryBiomass (ecology)BiologyChromatographyPulp and paper industryBotanyHeterotrophAlgaeEnvironmental engineeringEcologyEnvironmental scienceBacteriaCircadian rhythm

Abstract

fetched live from OpenAlex

In an effort to incorporate a novel bioremediation technology into minkery wastewater treatment systems, this study aimed to investigate the effect of light cycles on the simultaneous production of Chlorella vulgaris biomass, lipid, carbohydrate, and protein in controlled environment minkery wastewater. Diluted minkery cage-washing wastewater used in the experiments, which were designed as a completely randomised design with a single factor (light cycle) experiment. The single factor had four levels: six-day continuous light (photoautotrophic), 48-h light/24-h dark (mixotrophic), 24-h light/48-h dark (mixotrophic), and six-day continuous dark (heterotrophic) cycle, which were randomly assigned to the 2.25-litre vertical column controlled environment photobioreactors (PBR), each equipped with an independent cool-white fluorescent light (8 W, 6,700 K) operated at room temperature. The pH in each PBR was monitored and controlled using independent pH meters and air pumps. During six-day cultivations, continuous light and 48-h light/24-h dark cycles achieved the largest biomass (82.50 mg L−1 day−1 and 79.50 mg L−1 day−1, respectively) and protein productivities (42.62 mg L−1 day−1 and 38.79 mg L−1 day−1, respectively) out of Chlorella vulgaris in minkery wastewater; however, using 48-h light/24-h dark cycle instead of continuous light cycle would reduce the energy cost of cultivation. The light cycles of continuous light, 48-h light/24-h dark, and 24-h light/48-h dark achieved the highest lipid (15.06 mg L−1 day−1, 16.03 mg L−1 day−1, 12.82 mg L−1 day−1, respectively) and carbohydrate (11.15 mg L−1 day−1, 11.89 mg L−1 day−1, 8.08 mg L−1 day−1, respectively) productivities, and there was no statistically significant difference in between; however, using 24-h light/48-h light cycle would again reduce the energy cost of cultivation compared to the other two cycles. Overall, for the cultivation of Chlorella vulgaris in minkery wastewater, the most appropriate light cycle for the production of microalgae biomass and crude protein seems to be the mixotrophic growth under 48-h light/24-h dark cycle, while the mixotrophic growth of 24-h light/48-h dark cycle was the most appropriate system for the production of lipid and carbohydrate. Providing a supplemental organic carbon source, preferably from another waste stream, has a potential to change the story with respect to the competitiveness of continuous dark (heterotrophic) cycle, and studying other microalgae strains may make minkery wastewater even more competitive compared to traditional culture mediums.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.209
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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