Production of biomethane, biohydrogen, and volatile fatty acids from Nordic phytoplankton biomass grown in blended wastewater
Bibliographic record
Abstract
Upgrading carbon-negative microalgal biomass to biofuels and value-added products presents a three-pronged solution for waste treatment, carbon capture, and economically viable bioenergy production. Acidogenesis and methanogenesis are versatile processes at the core of anaerobic digestion systems, facilitating the conversion of diverse biogenic substrates into energy and a wide range of biobased products. The present study was conducted to integrate acidogenesis and methanogenesis for coproduction of biohydrogen, methane, and volatile fatty acids from Nordic phytoplankton consortia. For this purpose, microalgal consortia was cultivated in a raceway pond equipped with high surface area structures. Harvested microalgal biomass was subjected to thermoalkaline (2 % NaOH solution at 121 °C) and enzymatic (cellulase) pretreatments. The hydrolysates of the pretreated biomass were inoculated with thermally treated sludge for acidogenic fermentation and with mixture of untreated sludge and cow dung (1:1 v/v ratio) for anerobic digestion. The acidogenic process produced a significant amount of biohydrogen (maximum 164.8 L H2/VSload) along with volatile fatty acids (maximum 7.9 g COD/L), while methanogenesis resulted in methane production of maximum 210.7 mL CH4/VSload, accompanied by an ammonium recovery of 1278 mg NH4+/L. These maximum yields were all achieved by enzymatic pretreatment of the biomass fraction harvested from high surface area brush head structures inserted in the raceway pond. These results have important implications for designing phytoplankton cultivation systems and upstream pathways to optimize energy production from carbon-negative phytoplankton biomass.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".