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Record W2315469680 · doi:10.1021/ie501800j

Dynamic Modeling and Optimization of Batch Crystallization of Sugar Cane under Uncertainty

2014· article· en· W2315469680 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

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCrystallization and Solubility Studies
Canadian institutionsUniversity of Waterloo
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsNucleationCrystallizationWork (physics)Kinetic energyProcess optimizationVolume (thermodynamics)Process (computing)Constraint (computer-aided design)Scale (ratio)ThermodynamicsBiological systemMathematicsProcess engineeringMaterials scienceComputer scienceEnvironmental sciencePhysicsEngineeringEnvironmental engineering

Abstract

fetched live from OpenAlex

This work presents a study on the agitation rate effects on the average diameter (% volume D (4,3)) in the batch crystallization of sugar cane in pilot-scale process. The mathematical model presented in this work includes the population balance equation (PBE), the mass and energy balances, and the kinetics equations of nucleation and growth rate. The kinetic parameters were calculated from optimization using experimental data obtained from a pilot-scale process. An uncertainty analysis was performed and used to specify robust agitation trajectories that minimize the variations of crystal size from batch to batch. Four cases studies are presented to obtain 920, 1000, 1200, and 1300 μm of D (4,3) subject to a constraint in the formed crystal mass (FCM) of 4700 g under uncertainty in the kinetic parameters. The resulting robust agitation trajectories were implemented in the pilot-scale process. Comparisons between experimental data and the model predictions are presented.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.063
GPT teacher head0.317
Teacher spread0.254 · 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