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Record W4360786817 · doi:10.37867/te1402169

A REVIEW: WASTEWATER TREATMENT OF FOOD INDUSTRY BY SUSTAINABLE TECHNOLOGIES

2022· article· en· W4360786817 on OpenAlex
Harsh Patel, Dhara Bhavsar, Archana Mankad

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTowards Excellence · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsImpact
Fundersnot available
KeywordsWastewaterPopulationWaste managementSewage treatmentReuseEffluentBusinessEnvironmental scienceEngineeringNatural resource economicsEconomics

Abstract

fetched live from OpenAlex

Every single Continent is facing water scarcity and is looking at all viable solutions for minimising overuse of scarce freshwater resources. Many of the water sources that keep ecosystem flourishing and feed a growing population have become stressed due to man-made and natural causes. These natural resources will take time to renew and revive to sustain current situation.Prudently use and wastewater treatment is the only sustainable way to minimise the loss. In order to meet man's enormous requirements, industrial, agricultural, and household activities rise in tandem with population growth. In terms of production, consumption, export, and growth projections, the food industries play a significant role in the economic growth of many countries. After the industrial revolution and rapid urbanization results in consumption of water and also generation of wastewaterwhich is significantly distinct in nature, toxicity, and treatability. Traditional wastewater treatment technologies have been successful in treating effluents for disposal to some extent throughout the years. However, in order to reuse treated wastewater for industrial, agricultural, and home applications, advances in wastewater treatment technologies areurgently required. Membrane technologyhas become a popular solution for recovering water from a variety of wastewater sources. This article investigates the most popular membrane methods for wastewater treatment, as well as its merits and demerits.in this paper, Membrane fouling, cleaning, and modules are also discussed with appropriate recommendations are suggested. Usually, some standard wastewater treatment procedures, such as chemical coagulation, adsorption, and activated sludge, have been used for effluent treatmentowing to its cheap operating and maintenance costs, aerobic waste water treatment as a reductive medium is gaining popularity. Some novel technologies like Membrane Process (MP) have key advantages over the other technologies. They can possibly support sustainable industrial growth by saving energy, minimize environment impact, declining capital cost and enhancing raw material exploitation.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.016
GPT teacher head0.245
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it