Design of a pre-treatment integrated anaerobic digestion treatment facility for decarbonising whiskey industry: A circular economy perspective
Bibliographic record
Abstract
This paper outlines the sustainable management of the whiskey distillery by-products, known as draff and pot ale, through anaerobic digestion (AD). The substrates were pre-treated using 0.6M NaOH and high shear homogenizer for 24 h. Substrate pre-treatment helped improve the digestabilility of lignocellulosic materials towards AD. In addition, the complex cellulose, hemicellulose and lignin contents are disintegrated. A continuous stirred tank reactor with a volume of 1766 m 3 and organic loading rate of 4.04 kg COD/m 3 /day operating under mesophilic conditions for 30 days was designed to facilitate the complete digestion of the substrates. Compared with the conventional digesters, the proposed novel pre-treatment method achieved a 20% increase in methane yield. The energy recovery potential using a combined heat and power unit can cover 24 and 42.5% of the thermal and electrical demand of the distillery, respectively. The capital cost of the proposed facility was estimated as €3.6 million with a payback period of 9.60 years. In comparison, there is a payback period of 15.13 years without the pre-treatment. Additionally, this model decreases the distillery's carbon footprint by 33%. Although the proposed design applies to the distillery in Ireland, results could be used to design distillery plants in other countries.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".