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Record W4416941319 · doi:10.3390/membranes15120366

The Evaluation of Ammonium Sulphate as a Potential Draw Solute in a Hybrid FO-RO Process to Concentrate Nutrients (NPK) from a Simulated Liquid Digestate—Part I: Deionized Water as a Feed Solution

2025· article· en· W4416941319 on OpenAlexafffund
Marsa Tolouei, Niloofar Abdehagh, Majid Sartaj, Boguslaw Kruczek

Bibliographic record

VenueMembranes · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsNatural Resources CanadaUniversity of Ottawa
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsForward osmosisOsmotic pressurePotassiumOsmosisProcess (computing)MembraneDigestateNutrientPhosphorus

Abstract

fetched live from OpenAlex

The ultimate objective of this research is to concentrate nutrients—nitrogen (N), phosphorus (P), and potassium (K)—and produce process water from a chemically pretreated liquid digestate using an FO-RO hybrid process. However, in this manuscript, we assessed the suitability of (NH4)2SO4 and NaCl as draw solutes in a series of FO experiments employing a commercial CTA membrane and DI water as the feed solution. We also examined the regeneration of (NH4)2SO4 in a series of RO experiments at various feed concentrations and pressures using a commercial polyamide (PA) thin-film composite (TFC) membrane, ACM4. Additionally, the RO experiments enabled the experimental determination of the osmotic pressure of (NH4)2SO4 at various feed concentrations, which is crucial for designing the FO part of the hybrid process. The CTA membrane exhibited a significantly greater selectivity for (NH4)2SO4 than for NaCl at any osmotic pressure. The RO experiments demonstrated the possibility of reconcentrating (NH4)2SO4 to 0.5 mol/L, with a corresponding water flux of 60 L h−1 m−2 at 40 bars. The experimentally determined osmotic pressures were lower than those predicted by van’t Hoff’s equation but were consistent with those reported in the literature using an indirect hygrometric method.

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)0.001
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 score0.178
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.289
Teacher spread0.276 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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