The effect of heat transfer characteristics of macromolecule fouling on heat exchanger surface: A dynamic simulation study
Bibliographic record
Abstract
Abstract At the city gate gas pressure reduction stations (CGSs), to prevent natural gas from forming a hydrate in the throttle valve, the natural gas is heated by the heater before reaching the pressure relief valve. Heat exchangers are an essential component of industrial processes that contribute significantly to total system energy. Since the element impacting heat exchanger performance is the fouling process, all fouling processes and models were dynamically simulated in this study. Through coding in the C++ language and simultaneous use of fluent functions, or, in other words, user‐defined function (UDF), fouling‐related models were defined for this software. The dynamic simulation was performed, and parameters such as fouling strength and layer thickness were calculated. The effects of changing operating conditions, such as gas inlet velocity, surface temperature, and fouling species concentration on fouling growth, were also evaluated. As the concentration of fouling species increased, the fouling rate also increased. The amount of supersaturation and fouling rate increased as the surface temperature increased. Due to the operational limitations of the system, to reduce the fouling rate, the gas inlet velocity should be as high as possible, and the fluid inlet temperature, surface temperature, and concentration of fouling species should be as low as possible. In this study, the required time to reach the efficiency of 70% of the heat exchanger was calculated using the modelling of this chamber, which was equivalent to 190 days. Additionally, the critical thickness of the fouling layer at this time was 3.5 cm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".