Skip to main content
SpringerLink
Log in

Modeling and comparative study of heat exchangers fouling in phosphoric acid concentration plant using experimental data

  • Original
  • Published:
Heat and Mass Transfer Aims and scope Submit manuscript

Abstract

Fouling still remains one of the most difficult problems for the use of heat exchangers. A methodological process of advanced analysis of experimental data on heat exchangers fouling allowing building predictive models is necessary to determine the fouling degree. Here, three different methods were used to predict the fouling resistance from some easily measurable variables of the system which are: Kern and Seaton, Partial Least Squares (PLS) and Artificial Neural Networks (ANN). Indeed, the fouling resistance was estimated according to the inlet and outlet temperature of the cold fluid, the temperature of the hot fluid, the density and the volume flow rate of the cold fluid and time for three types of heat exchangers, i.e. tubular stainless-steel and graphite blocks (Supplier (A) and Supplier (B)).The best modeling was determined by maximizing certain statistical accuracy indices. Results show that modeling by the use of Artificial Neural Networks is very performing compared with modeling by Partial Least Squares regression and Kern and Seaton. One of the key features of ANN model is their small levels of error in comparison with other models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

Abbreviations

AARD:

Absolute average relative deviation

MSE:

Mean square error

RMSE:

Root mean square error

R2 :

Determination coefficient

r2 :

Correlation coefficient

C1 :

Coefficient in Eq. (15), Pa−1.s−1

Cb :

Bulk concentration of particles, kg.m−3

Cp:

Specific heat capacity, J.Kg−1.K−1

Cw :

Concentration of particles at the wall, kg.m−3

F:

Correction Factor (=1 for a steam condenser)

g:

Gravity acceleration (=9.81 m.s−2)

HMT:

Total manometric head, m

kp :

Mass transfer coefficient, m.s−1

mp :

Mass of deposited particles per surface area, kg.m−2

ṁ:

Mass flow rate, kg.s−1

N:

Observation number

P:

Pressure, bar

Q:

Thermal power,W

Rf:

Fouling resistance, m2.K.W−1

A:

Area, m2

T:

Temperature, K

t:

Time, h

U:

Global exchange coefficient, W.m−2.K−1

\( \dot{v} \) :

Volume flow rate, m3.h−1

ρ :

Density, kg.m−3

Φd :

Particle deposition rate, kg.m−2.s−1

Φr :

Particle removal rate, kg.m−2.s−1

τ :

Shear stress, Pa

ϴ :

Time required to reach 63.2% of Rf*, h

Δ:

Difference of greatness between two points

ac:

Acid

b:

Bulk

cir:

Circulation

d:

Deposition

dis:

Discharge

exp:

Experimental

in:

Input

ml:

Logarithmic mean

0:

clean

out:

Output

p:

Particle

r:

Removal

st:

Steam

suc:

Suction

w:

Wall

*:

Asymptotic value

References

  1. Demasles H, Mercier P, Tochon P, Thonon B (2007) Guide de l’encrassement des échangeurs de chaleur. Editions GRETh. France

  2. Ogbonnaya SK, Ajayi OO (2017) Fouling phenomenon and its effect on heat exchanger: a review. Frontiers in heat and mass transfer 9:1–12

    Google Scholar 

  3. Jradi R, Fguiri A, Marvillet C, Jeday MR (2019) Tubular heat exchanger fouling in phosphoric acid concentration process. Inverse Heat Conduction and Heat Exchangers. Intechopen edition; 1–16. https://www.intechopen.com/online-first/tubular-heat-exchanger-fouling-in-phosphoric-acid-concentration-process

  4. Muller-Steinhagen H, Reza Malayeri MR, Watkinson AP (2015) 10th International Conference on Heat Exchanger Fouling and Cleaning—2013 Budapest, Hungary. Heat transfer engineering 36:621–622

    Article  Google Scholar 

  5. Ansari Md IA, Sahoo PK, Datta AK (2012) Milk fouling simulation in a triple tube heat exchanger. Journal of Agricultural Engineering 49:1–11

    Google Scholar 

  6. Muller-Steinhagen H (2011) Heat transfer fouling: 50 years after the Kern and Seaton model. Heat Transfer Engineering 32:1–13

    Article  Google Scholar 

  7. Wang Y, Yuan Z, Liang Y, Xie Y, Chen X, Li X (2015) A review of experimental measurement and prediction models of crude oil fouling rate in crude refinery preheat trains. Asia Pac J Chem Eng 10:607–625

    Google Scholar 

  8. Mousavian S, Malayeri MR, Fahiminia F (2020) Mathematical modeling of initial fouling rate of calcium sulphate dehydrate based on nucleation rate. Heat and Mass Transfer. https://doi.org/10.1007/s00231-020-02847-4

  9. Ishiyama EM, Pugh SJ, Wilson DI (2019) Incorporating deposit ageing into visualization of crude oil preheat train fouling. Process integration and optimization for sustainability. https://doi.org/10.1007/s41660-019-00104-8

  10. Diaz-Bejarano E, Coletti F, Macchietto S (2019) Modeling and prediction of shell-side fouling in shell and tube heat exchangers. Heat Transfer Engineering 40:845–861. https://doi.org/10.1080/01457632.2018.1446814

    Article  Google Scholar 

  11. Polley GT, Wilson DI, Yeap LB, Pugh SJ (2002) Use of crude oil threshold data in heat exchanger design. Appl Therm Eng 22:763–776

    Article  Google Scholar 

  12. Rodriguez C, Smith R (2007) Optimization of operating conditions for mitigating fouling in heat exchanger networks. Chem Eng Res Des 85:839–851

    Article  Google Scholar 

  13. Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H (2018) State of the art in artificialneural networkapplications: a survey. Heliyon 4:1–41

    Article  Google Scholar 

  14. Davoudi E, Vaferi B (2018) Applying artificial neural networks for systematic estimation of degree of fouling in heat exchangers. Chem Eng Res Des 130:138–153

    Article  Google Scholar 

  15. Moya-Rico JD, Molina AE, Belmonte JF, Corcoles Tendero JI (2019) Almendros-Ibanez. Characterization of a triple concentric-tube heat exchanger with corrugated tubes using artificial neural Netwoek. Appl Therm Eng 147:1036–1046

    Article  Google Scholar 

  16. Biyanto TR (2016) Fouling resistance prediction using artificial neural network non linear auto-regressive with exogenous input model based on operating conditions and fluid properties correlations. In: AIP Conference Proceedings, p. 1737

  17. Schrodter K, Bettermann G, Staffel T, Klein T, Hofmann T (2000) Phosphoric acid and phosphates. Ullmann’s Encyclopedia of Industrial Chemistry. Wiley-VCH Verlag GmbH & Co edition

  18. Weber C, Tremeac B, Marvillet C, Castelain C (2016) Analysis of different fouling predictive models in a heat exchanger from experimental data. In:Proc ECOS conf efficiency-cost-optimization-simulation and environmental impact of energy systems. Slovenia: Portoroz

  19. Jradi R, Fguiri A, Marvillet C, Jeday MR (2019) Experimental analysis of heat transfer coefficients in phosphoric acid concentration process. Journal of Statistical Mechanics Theory and Experiment 2019:1–15. https://doi.org/10.1088/1742-5468/ab2531

  20. Gholami E, Vaferi B, Ariana MA (2018) Prediction of viscosity of several alumina-based nanofluids using various artificial intelligence paradigms comparison with experimental data and empirical correlations. Powder Technol 323:495–506

    Article  Google Scholar 

  21. Weber C (2018) Développement de méthodes d’analyse avancées de données expérimentales sur les phénomènes d’encrassement d’échangeurs thermiques en conditions réelles de fonctionnement. Université de Nantes, France

    Google Scholar 

  22. Peyghambarzadeh SM, Vatani A, Jamialahmadi M (2012) Experimental study of micro-particle fouling under forced convective heat transfer. Braz J Chem Eng 29:713–724

    Article  Google Scholar 

  23. Ilaboya IR, Igbinedion OE (2019) Performance of multiple linear regression (MLP) and artificial neural network (ANN) for the prediction of monthly maximum rainfall in Benin City, Nigeria. International Journal of Engineering Science and Application 3:21–37

    Google Scholar 

  24. Al-Haj IH (2012) Fouling in heat exchangers. MATLAB – a fundamental tool for scientific computing and engineering applications. Intechopen edition 3:57–96

    Google Scholar 

  25. Davoudi E, Vaferi B (2017) Applying artificial neural networks for systematic estimation of degree of fouling in heat exchangers. Chem Eng Res Des 130:138–153

    Article  Google Scholar 

  26. Awad MM (2012) Impact of flow velocity on surface particulate fouling - theoretical approach. Journal of American Science 8:442–449

    Google Scholar 

  27. Trzcinski P, Markowski M (2018) Diagnosis of the fouling effects in a Shell and tube heat exchanger using artificial neural network. Chem Eng Trans 70:355–360

    Google Scholar 

  28. Gautam R.K, Nirmal K.S, Parmar N, Vyas BG (2017) Effect of fouling on thermal and hydraulic parameter of Shell and tube heat exchanger, student conference 2017|Czech Technical University in Prague

  29. Bayat M, Aminian J, Bazmi M, Shahhosseini S, Sharifi K (2012) CFD modeling of fouling in crude oil pre-heaters. Energy Convers Manag 64:344–350

    Article  Google Scholar 

  30. Gurney K (2014) An introduction to neural networks. Taylor & Francis e-library, New York

  31. Zhang Z, Wu Z, Rincon D, Christofides PD (2019) Real time optimization and control of nonlinear processes using machine learning. Mathematics 7:1–25

    Google Scholar 

  32. Rafiq M, Bugmann G, Easterbrook D (2001) Neural network design for engineering applications. Comput Struct 79:1541–1552

    Article  Google Scholar 

  33. Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43:3–31

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Laboratory “Process, Energy, Environment & Electrical Systems”, National Engineering School of Gabès, Gabès, Tunisia

    Rania Jradi & Mohamed Razak Jeday

  2. CMGPCE Laboratory, French Institute of Refrigeration (IFFI), National Conservatory of Arts and Crafts (CNAM), Paris, France

    Christophe Marvillet

Authors
  1. Rania Jradi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christophe Marvillet
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohamed Razak Jeday
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rania Jradi.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jradi, R., Marvillet, C. & Jeday, M.R. Modeling and comparative study of heat exchangers fouling in phosphoric acid concentration plant using experimental data. Heat Mass Transfer 56, 2653–2666 (2020). https://doi.org/10.1007/s00231-020-02888-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00231-020-02888-9

Keywords

Search

Navigation