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Record W2139408248 · doi:10.1002/ceat.201200445

Wastewater Minimization in Pulp and Paper Industries through Energy‐Efficient Reverse‐Osmosis Membrane Processes

2013· article· en· W2139408248 on OpenAlex

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

VenueChemical Engineering & Technology · 2013
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsReverse osmosisWastewaterWaste managementEnvironmental scienceReverse osmosis plantDesalinationKraft processPaper millKraft paperEngineeringEnvironmental engineeringPulp and paper industryChemistryEffluentMembrane

Abstract

fetched live from OpenAlex

Abstract Fresh water consumption and wastewater management is a mandatory task to conserve water resources and to reduce wastewater discharge from chemical production processes. Such objectives have been addressed in many industrial sectors which consume large amounts of fresh water. Possibilities for reducing wastewater volumes by regeneration and recycling routes in the pulp and paper industry are analyzed. During pulp preparation and paper making processes by the Kraft pulping method a large amount of water is required to deliver the finished paper product. Reverse osmosis (RO) is applied for the analyses as an interception separation technology to reduce salt concentrations in wastewater streams for recycling purposes. The RO network synthesis problem is formulated as mixed integer nonlinear programming model which is solved using the general algebraic modeling system. A preliminary cost estimate indicates economic incentives by installation of RO units to avoid wastewater discharge and generate relatively clean water streams for inter‐plant usage.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.168
Teacher spread0.163 · 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