Analysis of the Mathematical Models for Identifying the Thickness of the Fouling Layer in Natural Gas Coolers

• Published in: Applied sciences 2024-05, Vol.14 (10), p.4003
• Main Authors: Čarnogurská, Mária, Příhoda, Miroslav, Andrejiová, Miriam, Tóth, Lukáš
• Format: Article
• Language: English
• Subjects: Analysis; balance method; Cooling; cooling surface area; Dimensional analysis; fouling layer thickness; Heat exchangers; Heat transfer; mathematical model; Mathematical models; Methods; Natural gas; natural gas cooler; Sediments; Temperature
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14104003

This article presents an analysis of three different approaches to the identification of the thickness of the fouling layer inside the pipes of natural gas (NG) coolers. At present, there is no existing simple analytical procedure for the identification of the fouling layer thickness. The authors of this article describe in detail the balance method, which required the use of a large number of physical parameters, changes in their sizes depending on the output temperature of the gas, the temperature of the cooling air, the air quantity, as well as the physical properties of both media. The computational model was robust, and its disadvantage was the iterative computation. The second analysed method was a dimensional analysis. It was applied using the Buckingham’s theorem to express the individual similarity criteria. In this method, 10 simplexes and two complexes were created. The fouling layer thickness, expressed using a derived criterial equation, exhibited real results. The third analysed method was based on analysing selected physical parameters with the use of a multiple regression analysis in MinitabX 18 software. The analysis showed that the fouling layer thickness depended on fewer parameters than the number of parameters assumed in the dimensional analysis or the balance method. The standard deviation that was identified in the multiple linear regression for a double crossflow cooler was 0.0667 and the value of reliability (the coefficient of determination of the multiple linear regression) R2 was 0.9985.