Production of chemicals and utilities in-house improves the environmental sustainability of phytoplankton-based biorefinery
Bibliographic record
Abstract
Life cycle assessment was used to evaluate the environmental impacts of phytoplanktonic biofuels as possible sustainable alternatives to fossil fuels . Three scenarios were examined for converting planktonic biomass into higher-value commodities and energy streams using the alga Scenedesmus sp. and the cyanobacterium Arthrospira sp. as the species of interest. The first scenario (Sc-1) involved the production of biodiesel and glycerol from the planktonic biomass. In the second scenario (Sc-2), biodiesel and glycerol were generated from the planktonic biomass, and biogas was produced from the residual biomass. The process also involved using a catalyst derived from snail shells for biodiesel production. The third scenario (Sc-3) was similar to Sc-2 but converted CO 2 from the biogas upgrading to methanol, which was then used in synthesizing biodiesel . The results indicated that Sc-2 and Sc-3 had a reduced potential (up to 60 % less) for damaging human health compared to Sc-1. Sc-2 and Sc-3 had up to 61 % less environmental impact than Sc-1. Sc-2 and Sc-3 reduced the total cumulative exergy demand by up to 44 % compared to Sc-1. In conclusion, producing chemicals and utilities within the biorefinery could significantly improve environmental sustainability , reduce waste, and diversify revenue streams.
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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) | 0.000 | 0.000 |
| 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) | 0.001 | 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".