Distillery decarbonisation and anaerobic digestion: balancing benefits and drawbacks using a compromise programming approach
Bibliographic record
Abstract
The anaerobic digestion (AD) of distillery by-products presents benefits such as greenhouse gas (GHG) emission savings and electricity savings, as well as drawbacks such as reduced animal feed and protein production and the potential import of animal feeds. This work balances these benefits and drawbacks using compromise programming (CP). The best combination of byproducts (from 9,261 scenarios) to use in AD was selected based on criteria chosen by management of a large distillery. The use of all by-products maximises benefits and drawbacks; the contrary also applies. When benefits and drawbacks are equally important, CP recommends using 50% of available draff, 50% of available thick stillage, and 55% of available thin stillage. The best combination when accounting for criteria weights chosen by distillery management is the use of 100% of available draff and 100% of available thick stillage. This could replace 48% of natural gas consumption at the distillery, reduce Scope 1 emissions by 45%, achieve a Scope 3 emissions savings of 22% of current Scope 1 emissions, and reduce electricity consumption in the feeds recovery plant of the distillery by 63%. Protein loss of 9,618 t could require the import of 19.59 kilo-tonne wet weight of material (ktwwt) of distillers grains and 9.15 ktwwt of soybean meal. If different criteria or criteria weights were used, a different result would be recommended. The methodology developed herein can aid in decarbonising the food and beverage industry by allowing decision-makers to balance the benefits and drawbacks of AD while accounting for subjective preferences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".