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Soft Computing

, Volume 22, Issue 3, pp 873–887 | Cite as

A neural network-based approach for steady-state modelling and simulation of continuous balling process

  • Mohammad Nadeem
  • Haider Banka
  • R. Venugopal
Methodologies and Application

Abstract

Efficiency of plant operations rely heavily on the stable availability of green pellets of desired size and quality. However, agglomeration plant often operate under capacity because of the sensitivity of balling circuits towards even the small perturbation in operating conditions. Though many researchers came up with various models to estimate the behaviour of continuous agglomeration system, there is still scope to develop improved modelling and simulation techniques. In this study, we present a neural network-based approach to simulate the nature of continuous balling process for better circuit control and improved plant efficiency. Mathematical expressions are developed to capture the response of produced and recycled load for a given set of parameters. Using these expressions, a multilayer perceptron model is trained that can predict the behaviour of circuit for pre-specified values of operating conditions. After simulation, effect of varying parameters on the dynamics of produced and recycled mass is summarized. Moreover, variations in process properties such as average recycled load, cycles needed to achieve steady state and maximum amplitude of recycled mass are also discussed.

Keywords

Continuous balling circuit Artificial neural network Simulation 

Notes

Compliance with ethical standards

Conflict of interest

We declare that there is no conflict of interests among authors.

Research involving human participants and/or animals (Ethical approval)

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Adetayo AA, Litster JD, Cameron IT (1995) Steady state modelling and simulation of a fertilizer granulation circuit. Comput Chem Eng 19(4):383–393CrossRefGoogle Scholar
  2. Barrasso D, Walia S, Ramachandran R (2013) Multi-component population balance modeling of continuous granulation processes: a parametric study and comparison with experimental trends. Powder Technol 241:85–97CrossRefGoogle Scholar
  3. Behzadi Sharareh Salar, Klocker Johanna, Hüttlin Herbert, Wolschann Peter, Viernstein Helmut (2005) Validation of fluid bed granulation utilizing artificial neural network. Int J Pharm 291(1):139–148CrossRefGoogle Scholar
  4. Behzadi SS, Prakasvudhisarn C, Klocker J, Wolschann P (2009) Comparison between two types of artificial neural networks used for validation of pharmaceutical processes. Powder Technol 195(2):150–157CrossRefGoogle Scholar
  5. Cameron IT, Wang FY, Immanuel CD, Stepanek F (2005) Process systems modelling and applications in granulation: a review. Chem Eng Sci 60(14):3723–3750CrossRefGoogle Scholar
  6. Capes CE, Mcllhinney AE, Coleman RD (1975) Some considerations on the dynamics of balling circuits. Soc Min Eng AIME 258:204–208Google Scholar
  7. Chang DH (1970) Steady-state behavior of continuous granulators—an elementary mathematical analysis. Chem Eng Sci 25(5):875–883CrossRefGoogle Scholar
  8. Cross M (1977) Mathematical model of balling-drum circuit of a pelletizing plant. Ironmak Steelmak 4(3):159–169Google Scholar
  9. Cross M, Wellstead PE (1978) Some control and simulation aspects of the pelletizing of iron ore. Simulation 30(2):55–61CrossRefGoogle Scholar
  10. Green DW, Perry RH (2008) Perrys chemical engineers handbook, 7th edn. McGraw-Hill, New YorkGoogle Scholar
  11. Han CD, Wilenitz I (1970) Mathematical modeling of steady-state behavior in industrial granulators. Ind Eng Chem Fundam. 9(3):401–411CrossRefGoogle Scholar
  12. Haykin S (2004) Neural network: a comprehensive foundation. Neural Netw 2:2004Google Scholar
  13. Iveson SM, Litster JD, Hapgood K, Ennis BJ (2001) Nucleation, growth and breakage phenomena in agitated wet granulation processes: a review. Powder Technol 117(1):3–39CrossRefGoogle Scholar
  14. Iveson SM (2002) Limitations of one-dimensional population balance models of wet granulation processes. Powder Technol 124(3):219–229CrossRefGoogle Scholar
  15. Kapur PC (1978) Balling and granulation. Adv Chem Eng 10:55–123CrossRefGoogle Scholar
  16. Kapur PC, Sastry KVS, Fuerstenau DW (1981) Mathematical models of open-circuit balling or granulating devices. Ind Eng Chem Process Des Dev 20(3):519–524CrossRefGoogle Scholar
  17. Kulju T, Paavola M, Spittka H, Keiski RL, Juuso Esko, Leiviskä Kauko, Muurinen Esa (2016) Modeling continuous high-shear wet granulation with dem-pb. Chem Eng Sci 142:190–200CrossRefGoogle Scholar
  18. Murtoniemi E, Yliruusi J, Kinnunen P, Merkku P, Leiviskä K (1994) The advantages by the use of neural networks in modelling the fluidized bed granulation process. Int J Pharm 108(2):155–164CrossRefGoogle Scholar
  19. Petrović J, Chansanroj K, Meier B, Ibrić S, Betz Gabriele (2011) Analysis of fluidized bed granulation process using conventional and novel modeling techniques. Eur J Pharm Sci 44(3):227–234CrossRefGoogle Scholar
  20. Ramachandran R, Chaudhury A (2012) Model-based design and control of a continuous drum granulation process. Chem Eng Res Des 90(8):1063–1073CrossRefGoogle Scholar
  21. Sastry KVS, Fuerstenau DW (1973) Mechanisms of agglomerate growth in green pelletization. Powder Technol 7(2):97–105CrossRefGoogle Scholar
  22. Sastry KVS, Fuerstenau DW (1975) Laboratory simulation of closed-circuit balling drum operation by locked-cycle experiments. Trans SME 258:335–340Google Scholar
  23. Servin M, Berglund T, Mickelsson K-O, Rönnbäck S, Wang D, Malmberget LKAB, R&D. (2015) Modeling and simulation of a granulation system using a nonsmooth discrete element method. In: ECCOMAS IV international conference on particle-based methods 2015Google Scholar
  24. Skapura D, Freeman JA (1991) Neural networks algorithms, applications, and programming techniques. Addison-Wesley Publishing Company, MassachusettszbMATHGoogle Scholar
  25. Venkataramana R, Kapur PC, Gupta SS (2002) Modelling of granulation by a two-stage auto-layering mechanism in continuous industrial drums. Chem Eng Sci 57(10):1685–1693CrossRefGoogle Scholar
  26. Wang FY, Zhang J, Litster JD, Cameron IT (1994) Physically based dynamic models of granulation circuits for process control and system optimization. In: First international particle technology forum, Colorado, USAGoogle Scholar
  27. Wang D, Servin M, Berglund T, Mickelsson K-O, Rönnbäck S (2015) Parametrization and validation of a nonsmooth discrete element method for simulating flows of iron ore green pellets. Powder Technol 283:475–487CrossRefGoogle Scholar
  28. Wang FY, Cameron IT (2002) Review and future directions in the modelling and control of continuous drum granulation. Powder Technol 124(3):238–253CrossRefGoogle Scholar
  29. Watano S, Takashima H, Miyanami K (1997) Scale-up of agitation fluidized bed granulation by neural network. Chem Pharm Bull 45(7):1193–1197CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian School of MinesDhanbadIndia
  2. 2.Department of Fuel and Mineral EngineeringIndian School of MinesDhanbadIndia

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