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Record W2275957728 · doi:10.2166/wqrjc.2015.143

Effect of pH level and acid type on total ammoniacal nitrogen (TAN) retention and fouling of reverse osmosis membranes processing swine wastewater

2015· article· en· W2275957728 on OpenAlexaffabout
L. Masse, Martin Mondor, J. Dubreuil

Bibliographic record

VenueWater Quality Research Journal · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsReverse osmosisChemistryWastewaterFoulingMembrane foulingMembranePermeationChromatographyAmmoniacal nitrogenPulp and paper industryChemical oxygen demandNitrogenEnvironmental engineeringEnvironmental scienceBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Wastewaters fed to reverse osmosis (RO) membranes sometimes need to be acidified to prevent inorganic fouling and increase total ammoniacal nitrogen (TAN) retention. In this project, the effect of pH level (6.5–7.1) and acid type (HCl vs. H2SO4) on membrane fouling and cleaning as well as permeate flux and quality during the processing of swine wastewater with a spiral-wound RO membrane was investigated. The use of H2SO4 to lower pH produced slightly higher permeate fluxes than HCl and there was no indication of sulfate precipitates on the membrane. Membrane fouling intensity and flux recovery upon cleaning were not affected by pH level or acid type. Lowering swine wastewater pH from 7.1 to 6.5 with HCl reduced TAN concentration in permeate from 142 to 59 mg/L. Using H2SO4 to lower pH to 6.5 further decreased TAN concentration to 39 mg/L. At pH 6.5 with both acid types, the concentration of unionized NH3 in the permeate was below the Canadian guideline of 0.019 mg/L for the release of wastewater to an aquatic environment. The use of H2SO4 would be recommended with swine wastewater, because of lower cost and volumetric input required to lower pH, as well as higher permeate quality and flux.

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)0.007
metaresearch head score (Gemma)0.001
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 score0.026
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.167
GPT teacher head0.393
Teacher spread0.226 · 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

Citations5
Published2015
Admission routes2
Has abstractyes

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