Life‐cycle assessment of full‐scale membrane bioreactor and tertiary treatment technologies in the fruit processing industry
Bibliographic record
Abstract
A life-cycle assessment (LCA) study was completed to assess the environmental impacts of an on-site wastewater treatment system in the fresh-cut fruit processing industry consisting of a membrane bioreactor (MBR), followed by reverse osmosis (RO) and ultraviolet (UV) disinfection. The system boundaries comprised raw materials extraction and processing, transportation, construction, operation, and waste disposal. SimaPro 8.0.4.26 was used as the software tool, supported by two impact assessment methods (ReCiPe v1.11 and TRACI v2.1). Analysis showed that the treatment capacity of the MBR and tertiary technologies contributed the least damage to the ecosystem when compared with the other three scenarios and can provide water for reuse. Treating wastewater in municipal wastewater treatment plants (WWTPs) mitigated eutrophication like the MBR system but resulted in more environmental impacts from climate change and human health when compared with the on-site treatment system. Findings will be informative to stakeholders in the fresh-cut agri-food sector seeking input into selecting the appropriate treatment approach, with water reuse a goal. PRACTITIONER POINTS: Life-cycle analysis was completed on a fruit processing facility using MBR + RO + UV. On site treatment with MBR + RO UV provides least amount of environmental impact. Use of MBR + RO + UV treatment on fruit wastewater allows for water reuse. ReCiPe v1.11 and TRACI v2.1 give similar LCA results, with TRACI recommended for North American analysis.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".