Development of cost models of algae production in a cold climate using different production systems
Bibliographic record
Abstract
Abstract Research into the potential use of microalgae to produce biofuels is receiving significant attention. In the cold climates of countries like Canada, algae cultivation in open raceway pond (ORP) systems is limited to a short period of the year when pond surface water temperatures and ambient light conditions enable optimal culture growth. In this study we develop techno‐economic assessment models to predict, evaluate, and compare the techno‐economic results from three autotrophic algae cultivation scenarios to produce algae biomass. The first is a modeled ORP site located in the southern USA, which has a minimum biomass selling price (MBSP) for algae of $541 tonne−1 (T−1). The second scenario models an identical ORP system co‐located at a site near Fort Saskatchewan, a northern city in the province of Alberta, Canada. The resulting MBSP is $1288 T−1. A third scenario models a photobioreactor (PBR) cultivation system co‐located at the same northern Alberta site and shows algae production with an MBSP of $550 T−1. Each system is scaled to produce 2000 T day−1 ash‐free dry weight (AFDE) algae biomass. The study concludes that PBR systems deployed at this scale have the potential to reduce production costs significantly ($ T−1) compared to similarly sited ORP systems in Canada, despite climatic factors and high initial capital costs associated with PBR construction. Furthermore, the modeled PBR system required 0.3% of the water required by the ORP cultivation platforms (153 × 103 versus 59 527 × 103 m3) and 0.04% of the land (32 versus. 82 038 ha). © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".