Modeling of CaSO4 crystallization fouling of finned tubes during nucleate pool boiling

Esawy, M.; Malayeri, M.R.

doi:10.1016/j.cherd.2016.11.030

Cited by 12 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An interesting model for CaSO 4 fouling of finned tubes during nucleate pool boiling which predict the micro layer super saturation under the bubbles as a function of geometry is presented. The model showed a good agreement with experimental data in the heat flux range of 100–300 kW m −2 4 .…”

Section: Introductionsupporting

confidence: 65%

Analysis and estimation of cross-flow heat exchanger fouling in phosphoric acid concentration plant using response surface methodology (RSM) and artificial neural network (ANN)

Jradi

Marvillet

Jeday

2022

Sci Rep

View full text Add to dashboard Cite

The production of phosphoric acid by dehydrated process leads to the precipitation of unwanted insoluble salts promoting thus the crystallization fouling build-up on heat transfer surfaces of the exchangers. During the acid concentration operation, the presence of fouling in heat exchangers results in reducing the performance of this equipment, in terms of heat transfer, while increasing energy losses and damaging the apparatus. To mitigate these adverse effects of fouling, it is necessary to forecast the thermal resistance of fouling to schedule and perform exchanger cleaning. In this context, artificial neural network and response surface methodology were used to estimate thermal resistance of fouling in a cross-flow heat exchanger by using the operating data of the concentration loop. The absolute average relative deviations, mean squared errors, root mean squared errors and correlation coefficients were used as indicators error between the experimental and estimated values for both methods. The best fitted model derived from response surface methodology method was second order polynomial while the best architecture topology, for the artificial neural network method, consists of three layers: input layer with six input variables, hidden layer with six hidden neurons and an output layer with single output variable. The interactive influences of operating parameters which have significant effects on the fouling resistance were illustrated in detail. The value of correlation coefficient for the output parameter from the response surface methodology is 0.9976, indicating that the response surface methodology as an assessment methodology in estimating fouling resistance is more feasible compared with the artificial neural network approach.

show abstract

Section: Introductionsupporting

confidence: 65%

Analysis and estimation of cross-flow heat exchanger fouling in phosphoric acid concentration plant using response surface methodology (RSM) and artificial neural network (ANN)

Jradi

Marvillet

Jeday

2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Hence, the induction period of Fe-Al MNS is extended, as shown in Figure 9. Moreover, the micro/nanostructures increased the boiling bubble activity, which creates a porous surface [41,42], and the detachment of the boiling bubble dislodged the loose fouling from the surface with wiping action [9,43]. As shown in Figure 14c,f, the Al proportion in the coating also influenced the antifouling property.…”

Section: Discussionmentioning

confidence: 90%

Fabrication of Fe‒Al Coatings with Micro/Nanostructures for Antifouling Applications

Wang

Fan

et al. 2020

Coatings

View full text Add to dashboard Cite

Fouling is one of the common problems in heat-transfer applications, resulting in higher fouling resistance, and lower heat-transfer coefficient. This paper introduces the design and fabrication of an Fe–Al coating with micro/nanostructures on low-carbon steel by electrical discharge coating (EDC) technology to improve the antifouling property. The Fe–Al coating with micro/nanostructures is characterized by a large number of micro/nanostructures and superior anti-fouling property, which is attributed to its hydrophobic surface. The antifouling property, fouling induction period and contact angle of the Fe–Al coating with micro/nanostructures increase with the increasing gap voltage. Compared with the polished surface of low-carbon steel, the Fe–Al coating with micro/nanostructures extends the induction period from 214 to 1350 min, with a heat flux of 98 kW·m−2. After 50 adhesion tests, the contact angle of the Fe–Al coating with micro/nanostructures decreases from 6.81% to 27.52%, which indicates that the Fe–Al coating with micro/nanostructures is durable and suitable for industrial applications.

show abstract

“…They included aging in the deposit removal term and thus the model is able to represent the sawtooth behaviour of the results. Esawy and Malayeri [84] proposed a model for CaSO 4 scaling of finned tubes during nucleate pool boiling. Microlayer supersaturation under the bubbles as a function of geometry was predicted and the model showed good agreement with experimental data in the heat flux range of 100-300 kW m −2 .…”

Section: Modelling Crystallization Foulingmentioning

confidence: 99%

A Review of Crystallization Fouling in Heat Exchangers

et al. 2021

View full text Add to dashboard Cite

A vast majority of heat exchangers suffer from unwanted deposition of material on the surface, which severely inhibits their performance and thus marks one of the biggest challenges in heat transfer. Despite numerous scientific investigations, prediction and prevention of fouling remain unresolved issues in process engineering and are responsible for large economic losses and environmental damage. This review article focuses specifically on crystallization fouling, providing a comprehensive overview of the state-of-the-art of fouling in heat exchangers. The fundamentals of the topic are discussed, as the term fouling resistance is introduced along with distinct fouling behaviour, observed in laboratory and industrial environments. Insight into subsequent phases of the fouling process is provided, along with the accompanying microscale events. Furthermore, the effects of fluid composition, temperature, flow velocity, surface condition, nucleate boiling and composite fouling are comprehensively discussed. Fouling modelling is systematically reviewed, from the early work of Kern and Seaton to recently used artificial neural networks and computational fluid dynamics. Finally, the most common fouling mitigation approaches are presented, including design considerations and various on-line strategies, as well as off-line cleaning. According to our review, several topics require further study, such as the initial stage of crystal formation, the effects of ageing, the interplay of two or more fouling mechanisms and the underlying phenomena of several mitigation strategies.

show abstract

Modeling of CaSO4 crystallization fouling of finned tubes during nucleate pool boiling

Cited by 12 publications

References 19 publications

Analysis and estimation of cross-flow heat exchanger fouling in phosphoric acid concentration plant using response surface methodology (RSM) and artificial neural network (ANN)

Analysis and estimation of cross-flow heat exchanger fouling in phosphoric acid concentration plant using response surface methodology (RSM) and artificial neural network (ANN)

Fabrication of Fe‒Al Coatings with Micro/Nanostructures for Antifouling Applications

A Review of Crystallization Fouling in Heat Exchangers

Contact Info

Product

Resources

About