Crude oil fouling in refinery preheat exchangers is a chronic operating problem that compromises energy recovery in these systems. Progress is hindered by the lack of quantitative knowledge of the dynamic effects of fouling on exchanger heat transfer and pressure drop. The concept of a thermal ‘fouling threshold’, first introduced by Ebert and Panchal, is revisited here alongside models of hydraulic effects of fouling to provide a graphical tool, the modified temperature field plot, for assessing chronic chemical reaction fouling effects in refinery heat exchangers. Fouling data of varying quality, collected from pilot plant and refinery operation, were compared with two previously published threshold fouling models and one based on the Epstein deposition model. The model by Epstein showed the best agreement, primarily because it can accommodate fouling that is mass transfer as well as reaction controlled. The hydraulic analysis indicated that the simple slab approximation for fouling layers gave a reasonably good mapping between heat transfer and pressure drop effects as long as roughness contributions are not significant. Where roughness effects (or tube blockage) are important, the relationship between thermal and hydraulic performance is not straightforward. A case study, based on the network described by Panchal and Huang-Fu, is used to illustrate the thermo-hydraulic effects of fouling and the application of the modified temperature field plot.
Steady state pool boiling heat flux data has been obtained for acetone-isopropanol-water and acetone-MEK(methyl ethyl ketone)-water ternary mixtures. The data shows that to obtain a given heat flux, the wall superheat required is greater for mixtures than for the pure components constituting the mixture. The measured heat transfer coefficients were compared with predictions from literature correlations for multicomponent mixtures. In all the cases, overestimation or underestimation of the data was observed. Therefore, a new correlation has been proposed for the heat flux in terms of Archimedes number, Prandtl number, surface–liquid interaction parameter, modified Jakob number, dimensionless surface roughness group, properties-profile parameter and an effective temperature driving force. In general, the effective temperature driving force in binary mixtures is less than that encountered in pure components and is obtained by incorporating the binary diffusivity of the mixture. In multicomponent systems, the multicomponent diffusion coefficients have to be incorporated into the expression for the effective temperature driving force. The heat flux correlation predicts the present experimental data as well as literature data, satisfactorily. The heat flux was found to be a function of the difference between the equilibrium vapour and liquid concentration, (y − x) of the light component(s) and the minimum heat flux occurs at the maximum of (y − x) of the light component(s).