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Record W3098808920 · doi:10.1016/j.wri.2020.100137

Advanced Oxidation Processes as sustainable technologies for the reduction of elderberry agro-industrial water impact

2020· article· en· W3098808920 on OpenAlexfundno aff
Leonor C. Ferreira, Irene Salmerón, José A. Peres, Pedro B. Tavares, Marco S. Lucas, S. Malato

Bibliographic record

VenueWater Resources and Industry · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsnot available
FundersH2020 European Research CouncilInstitute of Infection and ImmunityFundação para a Ciência e a Tecnologia
KeywordsBiodegradationWastewaterReuseEnvironmental scienceSewage treatmentActivity-based costingIndustrial wastewater treatmentWaste managementAdvanced oxidation processPulp and paper industryWater treatmentChemistryEnvironmental engineeringBusinessEngineering

Abstract

fetched live from OpenAlex

World is facing an environmental crisis and industrial awareness regarding their wastewaters is gaining strength. By treating them, water footprint can be reduced allowing their recycle and reuse, as well as recovering substances with added value. This work reports the pre-treatment of non-biodegradable elderberry agro-industrial wastewater at pilot plant scale by different oxidative processes (solar photo-Fenton, solar electrochemistry and ozonation). An economical approach has been addressed for improving biodegradability for a subsequent lower-cost biological treatment. Solar photo-Fenton process attained biodegradability in 30 min entailing 0.22 € m−3 of operation costs. Solar photoelectro-Fenton treatment turned industrial wastewater biodegradable after 20 min costing 0.11 € m−3, while solar-assisted anodic oxidation required 60 min and 0.12 € m−3. Solar processes combined with electrooxidation showed to be more promising options than ozonation for reducing water footprint of biorecalcitrant industrial wastewaters and moderate organic content, if an engagement between treatment time and operating costs is prioritized.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.294

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.247
Teacher spread0.230 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2020
Admission routes1
Has abstractyes

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