Fouling in Industrial Heat Exchangers: Formation, Detection and Mitigation

Jradi, Rania; Marvillet, Christophe; Jeday, Mohamed Razak

doi:10.5772/intechopen.102487

Cited by 8 publications

(4 citation statements)

References 32 publications

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“…where matrix 𝒀 = [ 𝑅 𝑓 [1] 𝑅 𝑓 [2] 𝑅 𝑓 [3] ], 𝑿 = [ 𝑅𝑒 𝑇 𝑓 𝛼 𝛽 𝜅 𝜎 ], 𝑩 = [ −0,25 −0,44 −0,49 0,08 0,05 0,10 0,48 0,51 0,53 0,34 0,43 0,56 0,30 0,24 0,12 −0,17 −0,33 −0,32] , and 𝒀 𝒄𝒐𝒏𝒔𝒕 = [ 2,61 2,61 2,61 ] and there are three regression outputs for each tested principal component. Parameters of 𝑅 𝑓 and 𝑠 tend to always close each other (redundant) in all principal component scenarios.…”

Section: Resultsmentioning

confidence: 99%

“…Heat exchanger has a crucial role as heat transfer equipment in many industrial processes. Various designs have the flexibility to adapt into many industrial processes based on thermodynamics properties, temperature, pressure, fluids, stream phase, density, chemical composition and viscosity [1]. As a preheat train in crude oil refining process, it is fed by heating fluid in the network coming from downstream products, which the recovered heat can be considered as energy saving [2].…”

Section: Introductionmentioning

confidence: 99%

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The Foundation and Validation of Sigmoid-Typed Functions for Fouling Formation Modelling of Crude Oil Flows

Gunawan Nugroho,

Novandion R. Kurniawan,

Totok R. Biyanto

et al. 2023

ARFMTS

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The phenomenological model of fouling formation is investigated in this research which, the parametrization method based on the species and energy equation is implemented. It is shown that the previous empirical sigmoid-typed correlation can also be generated from the species equation as a special solution. Meanwhile, the fouling heat resistance is obtained from the phenomenological energy equation. Correlating these two solutions, it is shown that the proposed model generates key parameters of fouling formation characteristics of crude preheat train within all operational conditions. Thus, it generalizes the previous models as it covers wider operational range.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Foundation and Validation of Sigmoid-Typed Functions for Fouling Formation Modelling of Crude Oil Flows

Gunawan Nugroho,

Novandion R. Kurniawan,

Totok R. Biyanto

et al. 2023

ARFMTS

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“…It afects heat transfer efciency, and hence the steam should be sent to heat the fuids for a long time due to ash fouling. Te heat exchanger's overall heat transfer coefcient may reduce signifcantly due to ash fouling [1]. Tis incurs cost, and it afects the demand of supply as well.…”

Section: Introductionmentioning

confidence: 99%

Statistical Model Identification and Variable Selection for Prediction of Heat Exchanger Fouling

Kumari

Starbino

Esther

et al. 2023

Mathematical Problems in Engineering

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Fly ash generated during boiler combustion are carried away with hot flue gas and deposited over the heat exchanger surfaces. This study was motivated by the decline in the thermal efficiency of the heat exchangers in thermal power plants due to ash fouling. The paper discusses a new computational procedure that has been attempted to identify the best statistical model for predicting heat exchanger fouling and also to choose the most strongly linked covariates. In this context, three different statistical models including multiple linear regression, sliced inverse regression, and random forests regression are used to estimate the cleanliness factor of the heat exchanger. The variables and model are chosen solely on the basis of analytical procedures. Heat exchanger metal tube temperature that highly influences the cleanliness factor is selected using a measure of importance based on random perturbations. Metal temperature above a threshold is selected as a strongly linked covariate. The R package modevarsel is used here to perform the statistical analysis. The value of root mean square error for the cleanliness factor from sliced inverse regression and random forest is 0.0002, indicating that as more feasible in predicting ash fouling.

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“…In the case of composite fouling; the crystallization can be severely influenced. The most commonly mechanism accompanying crystallization fouling was the particulate fouling 9 . A model for crystallization and particulate fouling prediction at different flow velocities and surface temperatures in plate heat exchangers with and without enhanced heat transfer was developed by Arsenyeva et al 10 .…”

Section: Introductionmentioning

confidence: 99%

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

Self Cite

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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.

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Fouling in Industrial Heat Exchangers: Formation, Detection and Mitigation

Cited by 8 publications

References 32 publications

The Foundation and Validation of Sigmoid-Typed Functions for Fouling Formation Modelling of Crude Oil Flows

The Foundation and Validation of Sigmoid-Typed Functions for Fouling Formation Modelling of Crude Oil Flows

Statistical Model Identification and Variable Selection for Prediction of Heat Exchanger Fouling

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

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