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The use of neural network to estimate mass transfer coefficient from the bottom of agitated vessel

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Abstract

In this study, the ability of the artificial neural network (ANN) to estimate the rate of mass transfer coefficient was compared against the mass transfer correlation obtained by dimensional analysis in terms of Sherwood, Schmidt and Reynolds numbers. The results showed that the ANN is better than the conventional mass transfer correlation in most cases and the best results are obtained at 3–7 neurons in the hidden layer.

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Abbreviations

A :

Area of mass transfer (cm2)

C :

Concentration (mol/cm3)

C 0 :

Initial concentration (mol/cm3)

C bO2 :

Bulk concentration of oxygen (mol/cm3)

C wO2 :

Wall concentration of oxygen (mol/cm3)

CR :

Corrosion rate (mm/year)

d :

Diameter of vessel (cm)

d i :

Diameter of impeller (cm)

D :

Diffusivity (cm2/s)

E i :

Experimental value

K :

Mass transfer coefficient (cm/s)

M :

Molecular weight (g/mol)

MRE :

Mean percent relative error

N :

Mole flux (mol/cm2 s)

n :

Impeller rotation speed (s−1)

P i :

Predicted value

Q :

Volume of solution (cm3)

Re :

Reynolds number (=ρnd 2i /μ)

RE :

Relative error

Sc :

Schmidt number (=μ/ρD)

Sh :

Sherwood number (=Kd/D)

STD R :

Standard deviation of the mean percent relative error

t :

Time (s)

X :

Number of samples

θ :

Angle of inclination of the bottom from the horizontal

μ :

Viscosity (g/cm s)

ρ :

Density (g/cm3)

References

  1. Cussler EL (1997) Diffusion: mass transfer in fluid systems. Cambridge University Press, Cambridge

    Google Scholar 

  2. Green DW, Perry RH (2008) Perry’s chemical engineers’ handbook, 8th edn. McGraw Hill, New York

    Google Scholar 

  3. Coulson JM, Richardson JF, Backhurst JR, Harker JH (1999) Coulson and Richardson’s chemical engineering volume 1—fluid flow, heat transfer and mass transfer, 6th edn. Elsevier

  4. McCabe WL, Smith JC, Harriott P (2005) Unit operations of chemical engineering, 7th edn. McGraw-Hill, Boston

    Google Scholar 

  5. Jakobsen HA (2008) Chemical reactor modeling: multiphase reactive flows. Springer, Berlin

    Google Scholar 

  6. Couper JR, Penney WR, Fair JR, Walas SM, James RC, Penney WR, James RF, Stanley MW (2005) Mixing and agitationchemical process equipment, 2nd edn. Gulf Professional Publishing, Burlington, pp 277–328

    Google Scholar 

  7. Ahmed SU, Ranganathan P, Pandey A, Sivaraman S (2010) Computational fluid dynamics modeling of gas dispersion in multi impeller bioreactor. J Biosci Bioeng 109:588–597

    Article  Google Scholar 

  8. Dakshinamoorthy D, Khopkar AR, Louvar JF, Ranade VV (2006) CFD simulation of shortstopping runaway reactions in vessels agitated with impellers and jets. J Loss Prev Process Ind 19:570–581

    Article  Google Scholar 

  9. Zughbi HD, Siddiqui SW, Fatehi AI (2006) Mixing in a fluid jet agitated tank: geometric effects. Dev Chem Eng Miner Process 14:143–152

    Article  Google Scholar 

  10. Paul EL, Atiemo-Obeng VA, Kresta SM (2004) Handbook of industrial mixing: science and practice. Wiley, Canada

    Google Scholar 

  11. Myerson AS (2002) Handbook of Industrial Crystallization, 2nd edn. Butterworth-Heinemann, Stoneham

    Google Scholar 

  12. Rodrίguez ME, Castillejos EAH, Acosta GFA (2007) Experimental and numerical investigation of fluid flow and mixing in pachuca tanks. Metall Mater Trans B 38:641–656

    Article  Google Scholar 

  13. Roy G, Shekhar R, Mehrotra S (1998) Particle suspension in (air-agitated) pachuca tanks. Metall Mater Trans B 29:339–349. doi:10.1007/s11663-998-0111-1

    Article  Google Scholar 

  14. Stitt EH (2002) Alternative multiphase reactors for fine chemicals: a world beyond stirred tanks? Chem Eng J 90:47–60

    Article  Google Scholar 

  15. Sedahmed GH, Farag HA, Kayar AM, El-Nashar IM (1998) Mass transfer at the impellers of agitated vessels in relation to their flow-induced corrosion. Chem Eng J 71:57–65

    Article  Google Scholar 

  16. El Shazly YMS (2011) Mass transfer controlled corrosion of baffles in agitated vessels. Corros Eng, Sci Technol 46:701–705

    Article  Google Scholar 

  17. Bourgeois W, Burgess JE, Stuetz RM (2001) On-line monitoring of wastewater quality: a review. J Chem Technol Biotechnol 76:337–348

    Article  Google Scholar 

  18. H-Jr Federsel (2009) Chemical process research and development in the 21st century: challenges, strategies, and solutions from a pharmaceutical industry perspective. Acc Chem Res 42:671–680

    Article  Google Scholar 

  19. Zhang SH, Moniz B, Meyer M (2006) ASM handbook volume 13C, corrosion: environments and industries. ASM International, pp 803–809

  20. Davis C, Frawley P (2009) Modelling of erosion–corrosion in practical geometries. Corros Sci 51:769–775

    Article  Google Scholar 

  21. El-Riedy MK (1981) Analogy between heat and mass transfer by natural convection from air to horizontal tubes. Int J Heat Mass Transf 24:365–369

    Article  Google Scholar 

  22. Malling GF, Thodos G (1967) Analogy between mass and heat transfer in beds of spheres: contributions due to end effects. Int J Heat Mass Transf 10:489–492 (IN485, 493–498)

    Article  Google Scholar 

  23. Ko B-J, Lee WJ, Chung B-J (2010) Turbulent mixed convection heat transfer experiments in a vertical cylinder using analogy concept. Nucl Eng Des 240:3967–3973

    Article  Google Scholar 

  24. Ambrosini W, Forgione N, Manfredini A, Oriolo F (2006) On various forms of the heat and mass transfer analogy: discussion and application to condensation experiments. Nucl Eng Des 236:1013–1027

    Article  Google Scholar 

  25. Bird RB, Stewart WE, Lightfoot EN (2006) Transport Phenomena. Wiley, New York

    Google Scholar 

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

    Article  Google Scholar 

  27. Verikas A, Bacauskiene M (2003) Using artificial neural networks for process and system modelling. Chemometr Intell Lab Syst 67:187–191

    Article  Google Scholar 

  28. Svozil D, Kvasnicka V, Pospichal J (1997) Introduction to multi-layer feed-forward neural networks. Chemometr Intell Lab Syst 39:43–62

    Article  Google Scholar 

  29. Taymaz I, Islamoglu Y (2009) Prediction of convection heat transfer in converging-diverging tube for laminar air flowing using back-propagation neural network. Int Commun Heat Mass Transf 36:614–617

    Article  Google Scholar 

  30. Jambunathan K, Hartle SL, Ashforth-Frost S, Fontama VN (1996) Evaluating convective heat transfer coefficients using neural networks. Int J Heat Mass Transf 39:2329–2332

    Article  MATH  Google Scholar 

  31. Thibault J, Grandjean BPA (1991) A neural network methodology for heat transfer data analysis. Int J Heat Mass Transf 34:2063–2070

    Article  Google Scholar 

  32. Zdaniuk GJ, Chamra LM, Keith Walters D (2007) Correlating heat transfer and friction in helically-finned tubes using artificial neural networks. Int J Heat Mass Transf 50:4713–4723

    Article  MATH  Google Scholar 

  33. Islamoglu Y, Kurt A (2004) Heat transfer analysis using ANNs with experimental data for air flowing in corrugated channels. Int J Heat Mass Transf 47:1361–1365

    Article  Google Scholar 

  34. Islamoglu Y (2003) A new approach for the prediction of the heat transfer rate of the wire-on-tube type heat exchanger–use of an artificial neural network model. Appl Therm Eng 23:243–249

    Article  Google Scholar 

  35. Wang Q, Xie G, Zeng M, Luo L (2006) Prediction of heat transfer rates for shell-and-tube heat exchangers by artificial neural networks approach. J Therm Sci 15:257–262

    Article  Google Scholar 

  36. García-Ochoa F, Castro EG (2001) Estimation of oxygen mass transfer coefficient in stirred tank reactors using artificial neural networks. Enzym Microb Technol 28:560–569

    Article  Google Scholar 

  37. Lemoine R, Fillion B, Behkish A, Smith AE, Morsi BI (2003) Prediction of the gas–liquid volumetric mass transfer coefficients in surface-aeration and gas-inducing reactors using neural networks. Chem Eng Process 42:621–643

    Article  Google Scholar 

  38. Lemoine R, Morsi BI (2005) An algorithm for predicting the hydrodynamic and mass transfer parameters in agitated reactors. Chem Eng J 114:9–31

    Article  Google Scholar 

  39. Lemoine R, Behkish A, Sehabiague L, Heintz YJ, Oukaci R, Morsi BI (2008) An algorithm for predicting the hydrodynamic and mass transfer parameters in bubble column and slurry bubble column reactors. Fuel Process Technol 89:322–343

    Article  Google Scholar 

  40. Shahsavand A, Derakhshan Fard F, Sotoudeh F (2011) Application of artificial neural networks for simulation of experimental CO2 absorption data in a packed column. J Nat Gas Sci Eng 3:518–529

    Article  Google Scholar 

  41. Cavas L, Karabay Z, Alyuruk H, Doğan H, Demir GK (2011) Thomas and artificial neural network models for the fixed-bed adsorption of methylene blue by a beach waste Posidonia oceanica (L.) dead leaves. Chem Eng J 171:557–562

    Article  Google Scholar 

  42. Cojocaru C, Macoveanu M, Cretescu I (2011) Peat-based sorbents for the removal of oil spills from water surface: application of artificial neural network modeling. Colloids Surf A 384:675–684

    Article  Google Scholar 

  43. Kumar KV, Porkodi K (2009) Modelling the solid–liquid adsorption processes using artificial neural networks trained by pseudo second order kinetics. Chem Eng J 148:20–25

    Article  Google Scholar 

  44. Yetilmezsoy K, Demirel S (2008) Artificial neural network (ANN) approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio (Pistacia vera L.) shells. J Hazard Mater 153:1288–1300

    Article  Google Scholar 

  45. Turan NG, Mesci B, Ozgonenel O (2011) The use of artificial neural networks (ANN) for modeling of adsorption of Cu(II) from industrial leachate by pumice. Chem Eng J 171:1091–1097

    Article  Google Scholar 

  46. Hernández JA (2009) Optimum operating conditions for heat and mass transfer in foodstuffs drying by means of neural network inverse. Food Control 20:435–438

    Article  Google Scholar 

  47. Hernández-Pérez JA, GarcÍa-Alvarado MA, Trystram G, Heyd B (2004) Neural networks for the heat and mass transfer prediction during drying of cassava and mango. Innov Food Sci Emerg Technol 5:57–64

    Article  Google Scholar 

  48. Lertworasirikul S, Saetan S (2010) Artificial neural network modeling of mass transfer during osmotic dehydration of kaffir lime peel. J Food Eng 98:214–223

    Article  Google Scholar 

  49. Ochoa-Martínez CI, Ayala-Aponte AA (2007) Prediction of mass transfer kinetics during osmotic dehydration of apples using neural networks. LWT Food Sci Technol 40:638–645

    Article  Google Scholar 

  50. Gregory DP, Riddiford AC (1960) Dissolution of copper in sulfuric acid solutions. J Electrochem Soc 107:950–956

    Article  Google Scholar 

  51. Poulson B, Robinson R (1986) The use of a corrosion process to obtain mass transfer data. Corros Sci 26:265–279

    Article  Google Scholar 

  52. Saroha AK (2010) Solid–liquid mass transfer studies in trickle bed reactors. Chem Eng Res Des 88:744–747

    Article  Google Scholar 

  53. Fouad YO, Malash GF, Zatout AA, Sedahmed GH (2013) Mass and heat transfer at an array of vertical tubes in a square stirred tank reactor. Chem Eng Res Des 91:234–243

    Article  Google Scholar 

  54. Kumaresan T, Joshi JB (2006) Effect of impeller design on the flow pattern and mixing in stirred tanks. Chem Eng J 115:173–193

    Article  Google Scholar 

  55. Mendham J, Denney RC, Barnes JD, Thomas MJK (2000) Vogel’s quantitative chemical analysis, 6th edn. Prentice Hall, Englewood Cliffs

    Google Scholar 

  56. Pickett DJ (1977) Electrochemical reactor design. Elsevier, New York

    Google Scholar 

  57. El-Shazly YM, Zahran RR, Farag HA, Sedahmed GH (2004) Mass transfer in relation to flow induced corrosion of the bottom of cylindrical agitated vessels. Chem Eng Process 43:745–751

    Article  Google Scholar 

  58. Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5:989–993

    Article  Google Scholar 

  59. Colorado D, Ali ME, García-Valladares O, Hernández JA (2011) Heat transfer using a correlation by neural network for natural convection from vertical helical coil in oil and glycerol/water solution. Energy 36:854–863

    Article  Google Scholar 

  60. Ní Mhurchú J, Foley G (2006) Dead-end filtration of yeast suspensions: correlating specific resistance and flux data using artificial neural networks. J Membr Sci 281:325–333

    Article  Google Scholar 

  61. Shyam SS (2001) A neural network approach for non-iterative calculation of heat transfer coefficient in fluid–particle systems. Chem Eng Process 40:363–369

    Article  Google Scholar 

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  1. Chemical Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt

    Yehia M. S. ElShazly

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ElShazly, Y.M.S. The use of neural network to estimate mass transfer coefficient from the bottom of agitated vessel. Heat Mass Transfer 51, 465–475 (2015). https://doi.org/10.1007/s00231-014-1430-1

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