Proposed design of distributed macroalgal biorefineries: thermodynamics, bioconversion technology, and sustainability implications for developing economies
Bibliographic record
Abstract
Abstract Biomass to fuel programs are under research and development worldwide. The largest biomass programs are underway in industrialized countries. In the coming decades, however, developing countries will be responsible for the major increase in transportation fuel demand. Although the lack of existing large‐scale infrastructure and primary resources preclude oil refining in developing countries, this provides an opportunity for the rapid implementation of small‐scale distributed biorefineries to serve multiple communities locally. The principles for biorefinery design, however, are still in their infancy. This review sets a precedent in combining thermodynamic, metabolic, and sustainability analyses for biorefinery design. We exemplify this approach through the design and optimization of a marine biorefinery for an average town in rural India. In this combined model, we include sustainability and legislation factors, intensive macro algae Ulva farming, and metabolic modeling of the biological two‐step conversion of Ulva feedstock by a yeast ( Saccharomyces cerevisiae ), and then by a bacterium ( Escherichia coli ), into bioethanol. We hope that the model presented here will be useful in considering practical aspects of biorefinery design. © 2013 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".