Comprehensive review of water management and wastewater treatment in food processing industries in the framework of water‐food‐environment nexus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Food processing is among the greatest water-consuming industries with a significant role in the implementation of sustainable development goals. Water-consuming industries such as food processing have become a threat to limited freshwater resources, and numerous attempts are being carried out in order to develop and apply novel approaches for water management in these industries. Studies have shown the positive impact of the new methods of process integration (e.g., water pinch, mathematical optimization, etc.) in maximizing water reuse and recycle. Applying these methods in food processing industries not only significantly supported water consumption minimization but also contributed to environmental protection by reducing wastewater generation. The methods can also increase the productivity of these industries and direct them to sustainable production. This interconnection led to a new subcategory in nexus studies known as water-food-environment nexus. The nexus assures sustainable food production with minimum freshwater consumption and minimizes the environmental destructions caused by untreated wastewater discharge. The aim of this study was to provide a thorough review of water-food-environment nexus application in food processing industries and explore the nexus from different aspects. The current study explored the process of food industries in different sectors regarding water consumption and wastewater generation, both qualitatively and quantitatively. The most recent wastewater treatment methods carried out in different food processing sectors were also reviewed. This review provided a comprehensive literature for choosing the optimum scenario of water and wastewater management in food processing industries.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it