Microalgae Commercialization Using Renewable Lignocellulose Is Economically and Environmentally Viable
Bibliographic record
Abstract
Conventional phototrophic cultivation for microalgae production suffers from low and unstable biomass productivity due to limited and unreliable light transmission outdoors. Alternatively, the use of a renewable lignocellulose-derived carbon source, cellulosic hydrolysate, offers a cost-effective and sustainable pathway to cultivate microalgae heterotrophically with high algal growth rate and terminal density. In this study, we evaluate the feasibility of cellulosic hydrolysate-mediated heterotrophic cultivation (Cel-HC) for microalgae production by performing economic and environmental comparisons with phototrophic cultivation through techno-economic analysis and life cycle assessment. We estimate a minimum selling price (MSP) of 4722 USD/t for producing high-purity microalgae through Cel-HC considering annual biomass productivity of 300 t (dry weight), which is competitive with the conventional phototrophic raceway pond system. Revenues from the lignocellulose-derived co-products, xylose and fulvic acid fertilizer, could further reduce the MSP to 2976 USD/t, highlighting the advantages of simultaneously producing high-value products and biofuels in an integrated biorefinery scheme. Further, Cel-HC exhibits lower environmental impacts, such as cumulative energy demand and greenhouse gas emissions, than phototrophic systems, revealing its potential to reduce the carbon intensity of algae-derived commodities. Our results demonstrate the economic and environmental competitiveness of heterotrophic microalgae production based on renewable bio-feedstock of lignocellulose.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".