Techno-Economic Assessment of Utilization of Cold Spraying Process for Fabrication of Resistive Heating Elements for Temperature Protection of Steel Pipes
Bibliographic record
Abstract
Abstract Design, manufacturing, and utilization of efficient heating systems for pipelines and closed-pressure equipment are necessary for cold regions to compensate for heat loss and prevent damages that are caused by freezing of the enclosed liquid. Given large-scale financial losses that stem from failure and bursting of the pipes, the development of novel, efficient, and affordable heaters, which can lead to improved efficiency, cost savings, and environmental benefits across various industries and applications, is of crucial importance. Heating systems have already been produced via different high-temperature thermal spraying techniques to achieve higher efficiency compared to conventional heating cables. In this study, tin, as the heating element, was deposited by using the cold spray process onto alumina coating that was fabricated by flame spraying (FS) to provide electrical insulation. Techno-economic assessment of fabrication and utilization of the coating-based heaters was conducted. It was found that cold-sprayed heater coatings exhibit improved performance compared to other thermally sprayed heater coatings and conventional heater cables. Further, their fabrication and utilization were more economically feasible. The results suggest that the new generations of coating-based heating systems may be competitive with conventional heat tracers that are widely used in industry.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".