Food Waste Digestate-Based Biorefinery Approach for Rhamnolipids Production: A Techno-Economic Analysis
Bibliographic record
Abstract
The present work evaluates the techno-economic feasibility of a rhamnolipids production process that utilizes digestate from anaerobic digestion (AD) of food waste. Technical feasibility, profitability and extent of investment risks between fermenter scale and its operating strategy for rhamnolipids production was investigated in the present study. Three scenarios were generated and compared: production using a single large fermenter (Scenario I), using two small fermenters operated alternately (Scenario II) or simultaneously (Scenario III). It was found that all the scenarios were economically feasible, and Scenario III was the most profitable since it allowed the most optimum fermenter operation with utilization of multiple small-scale equipment to reduce the downtime of each equipment and increase the production capacity and overall productivity. It had the highest net present value, internal rate of return and shortest payback time at a discount rate of 7%. Finally, a sensitivity analysis was conducted to indicate how the variation in factors such as feedstock (digestate) cost, rhamnolipids selling price, extractant recyclability and process capacity influenced the process economics. The work provides important insights on techno-economic performance of a food waste digestate valorization process which would be useful to guide its sustainable scale-up.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".