Bio-Valorization of CO <sub>2</sub> Using Microalgae: Techno-Economic Perspective
Bibliographic record
Abstract
Throughout the world, bio-sequestration of CO2 using microalgae is seen as a promising approach to produce biofuels and other value-added products (VAPs). However, the specifics of setting this up in Québec, particularly with regard to its climate, would necessitate adjustments and the development of new technologies. As such, Centre de recherche industrielle du Québec (CRIQ), in partnership with the National Optics Institute (NOI), proposed an R&D project to develop technology (CO2-Québec) at a cost that would make it financially viable. The approach consisted of reducing the surface footprint by a factor of 3 to 4 by amplifying the photosynthesis process. Mass and energy balances were carried out to complete the technoeconomic analysis. Finally, the business potential was evaluated by considering different approaches for the valorization of the biomass produced. The use of this technology to reduce greenhouse gases by transforming biomass into biofuels was proven to be unprofitable given current market prices for carbon and biofuels. For the moment, profitability can only be achieved by prioritizing the market for omega-3 content in lipids for the pharmaceutical, nutraceutical and food sectors. Furthermore, in the current context, there is a growing demand for bio-sourced building-block molecules for sustainable chemistry products, and many researchers are working on the development and optimization of technologies for transforming 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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".