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Record W2559133852 · doi:10.3303/cet1652098

Investigation of Fouling in Plate Heat Exchangers at Sugar Factory

2016· article· en· W2559133852 on OpenAlexaboutno aff
Alexey Demirskyy, Petro Kapustenko, Gennadii Khavin, Оlga Arsenyeva, Oleksandr I. Matsegora, Sergey Kusakov, I. Bocharnikov

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typearticle
Languageen
FieldEngineering
TopicFreezing and Crystallization Processes
Canadian institutionsnot available
Fundersnot available
KeywordsFoulingFactory (object-oriented programming)Sugar factoryHeat exchangerSugarPulp and paper industryWaste managementChemistryEnvironmental scienceMechanical engineeringEngineeringFood scienceComputer scienceBiochemistryMembrane

Abstract

fetched live from OpenAlex

The fouling formation in heat transfer equipment is the complex process, which is determined by the physical properties of the heat carrier, material of the unit and hydraulic characteristics of the flow. The mathematical model based on the asymptotic behaviour of water fouling is examined. The fouling process supposes the net rate of fouling accumulation as the difference between the fouling deposition rate and the fouling removal rate. The relation for predicting the fouling resistance dynamics during the time is proposed. The investigation of precipitation and particulate deposition in purified juice heating PHE for the first stage evaporation, which operates in sugar plant, was examined. In this position ?15? plate heat exchanger produced by Alfa Laval is used. The analysis and mathematical simulation of the experimental data are presented. For the juice heaters the content of fouling deposition is mostly the calcium salts as calcium carbonates and sulphates. The parameters of the equation for deposition term estimation were determined for the regarded heat carrier. It allows to determine the deposition term and to simulate the fouling formation in time. The comparison of the experimental data and mathematical calculations showed a good agreement. The proposed mathematical model enables to predict the fouling formation behaviour in PHE as purified juice heater and to determine the operation term for the cleaning of this unit. Basing on the observed model, the software, which enables to determine the periods of PHE cleaning during the operation was developed. The comparison of the industrial measurement data with calculation results is 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.209
GPT teacher head0.454
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2016
Admission routes1
Has abstractyes

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