Technoeconomic evaluation of protein‐rich animal feed and ethanol production from palm kernel cake
Bibliographic record
Abstract
Abstract Indonesia and Malaysia are net importers of animal feed products to meet the demand of their domestic livestock industries. These countries are also the largest producers and exporters of palm kernel cake (PKC), an animal feed waste by‐product from the palm industry that is used primarily as a ruminant feed. Prior work demonstrated that the bioethanol process can convert the gluco‐mannan fiber in PKC into ethanol and a high‐protein animal feed. We used Microsoft Excel to develop a bioethanol process model for PKC by adapting the well developed corn ethanol process used in the USA. The PKC biorefinery model, using PKC's composition and specialized enzymes to produce fermentable glucose and mannose, projects that 1 kg of PKC can produce 0.58 kg of high‐protein animal feed and 0.20 kg of ethanol, with some residual palm kernel oil. The model estimated an increase in crude protein content from 17% in the PKC to 27% in the high‐protein animal feed. A comprehensive technoeconomic assessment using the results of the process model indicates that a PKC bioethanol factory converting 100 000 Mg year–1 of PKC would cost USD 55 million and generate a 23% project internal rate of return (IRR). © 2021 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".