Overview of Some Recent Advances in Improving Water and Energy Efficiencies in Food Processing Factories
Bibliographic record
Abstract
Rapid development of food factories in both developed and developing countries, owing to continued growth in the world population, plays a critical role in the food supply chain, including environmental issues such as pollution, emissions, energy and water consumption, and thus food system sustainability. The objective of this study was to briefly review various environmental aspects of food processing operations, including aquatic, atmospheric, and solid waste generation, and also to discuss several strategies that many companies are using to reduce these negative impacts as well as to improve water and energy efficiency. To obtain higher energy efficiencies in food processing factories, two key operations can play critical roles: non-thermal processing (e.g., high pressure processing) and membrane processes. For higher water efficiency, reconditioning treatments resulting in water reuse for other purposes can be conducted through chemical and/or physical treatments. With regards to reducing volumes of processing food waste, two approaches include value-added by-product applications (e.g., animal feed) and/or utilization of food waste for energy production. Finally, we present trends for lowering operational costs in food processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".