Analysis of the economic viability and environmental impacts of a conceptual process for the recovery of lactic acid from spent media in cultivated meat production
Bibliographic record
Abstract
Abstract The scaled production of cultivated meat implies a future where large amounts of liquid waste in the form of spent media will be co-produced. Recycling of spent media, specifically certain abundant metabolites such as lactic acid, offers an opportunity for valorization and to offset the carbon footprint of cultivated meat production, however, the feasibility of recovering lactic acid from spent media has yet to be examined in detail. In this study, we developed a conceptual design of a five-step lactic acid recovery process integrated into a previously modeled cultivated meat facility. We examined the corresponding cost and environmental impacts of recovering an 88% aqueous, polymer-grade lactic acid solution and compared these footprints to data from commercial lactic acid fermentation processes. At an anticipated lactic acid concentration in spent media of 3 g/L, we found that the net cost of recovery would be $0.71 per kg of 88% lactic acid, with a 7.5 year simple payback period. Sales of this co-product could offset $0.06/kg of the cost associated with the production of cultivated meat. Depending on allocation scenarios, the environmental impact of the recovery process had a-1.0 to +0.2 kg CO 2 eq effect on the overall carbon footprint and a-22 to +3 MJ effect on cumulative energy demand per kg of cultivated meat production. These results suggest that the recovery of lactic acid may be an economically viable and environmentally beneficial practice if implemented in future production facilities. This study provides crucial guidance for lactic acid valorization and other media recycling strategies for cultivated meat that can be applied to broader animal cell biomanufacturing industries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".