Beyond tradition: charting a greener future for cassava starch industry using multi-criteria decision-making
Bibliographic record
Abstract
Cassava, a staple food crop, is widely used for starch production, but its inconsistent supply, price volatility, and substantial waste generation pose challenges to the cassava industrial market's growth. This study aims to identify a sustainable biorefinery pathway by optimizing conventional cassava starch plants (business-as-usual, BAU) for economic and environmental benefits. Four scenarios were evaluated: animal feed from peel waste (Scenario 1), fungal protein from thippi waste (Scenario 2), fish feed from digested wastewater (Scenario 3), and conversion of all waste streams into animal feed, fish feed, and fungal protein (Scenario 4). Scenario 4 emerged as the best pathway using the multi-criteria decision-making (MCDM) approach, with a performance score of 0.282. Despite the highest energy consumption (18.91 MWh), Scenario 4 was favored for producing four value-added products alongside starch, yielding the highest profit at USD 8.85 million. In contrast, profits for BAU, Scenario 1, Scenario 2, and Scenario 3 were 1.91, 2.30, 5.01, and USD 8.79 million, respectively. Waste valorization in Scenarios 1, 2, 3, and 4 resulted in CO2 avoidance of 36.5., 42.6., 21.7, and 57.45 t CO2eq., respectively. However, producing value-added products increased energy consumption by 13, 73, 7, and 74% compared to BAU (4.58 MWh). The global warming potential analysis showed negative values for scenarios 2 and 4, at -436 and -434 kg CO2eq./t root, respectively. This study highlights the potential of a biorefinery approach for sustainable cassava starch production, providing insights for future research and policy development.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".