Skip to main content
SpringerLink
Log in

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

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

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.

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

Similar content being viewed by others

References

  • Adetayo AA, Litster JD, Cameron IT (1995) Steady state modelling and simulation of a fertilizer granulation circuit. Comput Chem Eng 19(4):383–393

    Article  Google Scholar 

  • 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 241:85–97

    Article  Google Scholar 

  • 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):139–148

    Article  Google Scholar 

  • 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):150–157

    Article  Google Scholar 

  • Cameron IT, Wang FY, Immanuel CD, Stepanek F (2005) Process systems modelling and applications in granulation: a review. Chem Eng Sci 60(14):3723–3750

    Article  Google Scholar 

  • Capes CE, Mcllhinney AE, Coleman RD (1975) Some considerations on the dynamics of balling circuits. Soc Min Eng AIME 258:204–208

    Google Scholar 

  • Chang DH (1970) Steady-state behavior of continuous granulators—an elementary mathematical analysis. Chem Eng Sci 25(5):875–883

    Article  Google Scholar 

  • Cross M (1977) Mathematical model of balling-drum circuit of a pelletizing plant. Ironmak Steelmak 4(3):159–169

    Google Scholar 

  • Cross M, Wellstead PE (1978) Some control and simulation aspects of the pelletizing of iron ore. Simulation 30(2):55–61

    Article  Google Scholar 

  • Green DW, Perry RH (2008) Perrys chemical engineers handbook, 7th edn. McGraw-Hill, New York

    Google Scholar 

  • Han CD, Wilenitz I (1970) Mathematical modeling of steady-state behavior in industrial granulators. Ind Eng Chem Fundam. 9(3):401–411

    Article  Google Scholar 

  • Haykin S (2004) Neural network: a comprehensive foundation. Neural Netw 2:2004

    Google Scholar 

  • 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):3–39

    Article  Google Scholar 

  • Iveson SM (2002) Limitations of one-dimensional population balance models of wet granulation processes. Powder Technol 124(3):219–229

    Article  Google Scholar 

  • Kapur PC (1978) Balling and granulation. Adv Chem Eng 10:55–123

    Article  Google Scholar 

  • Kapur PC, Sastry KVS, Fuerstenau DW (1981) Mathematical models of open-circuit balling or granulating devices. Ind Eng Chem Process Des Dev 20(3):519–524

    Article  Google Scholar 

  • 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 142:190–200

    Article  Google Scholar 

  • 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):155–164

    Article  Google Scholar 

  • 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):227–234

    Article  Google Scholar 

  • Ramachandran R, Chaudhury A (2012) Model-based design and control of a continuous drum granulation process. Chem Eng Res Des 90(8):1063–1073

    Article  Google Scholar 

  • Sastry KVS, Fuerstenau DW (1973) Mechanisms of agglomerate growth in green pelletization. Powder Technol 7(2):97–105

    Article  Google Scholar 

  • Sastry KVS, Fuerstenau DW (1975) Laboratory simulation of closed-circuit balling drum operation by locked-cycle experiments. Trans SME 258:335–340

    Google Scholar 

  • 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 2015

  • Skapura D, Freeman JA (1991) Neural networks algorithms, applications, and programming techniques. Addison-Wesley Publishing Company, Massachusetts

    MATH  Google Scholar 

  • 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):1685–1693

    Article  Google Scholar 

  • 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, USA

  • 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 283:475–487

    Article  Google Scholar 

  • Wang FY, Cameron IT (2002) Review and future directions in the modelling and control of continuous drum granulation. Powder Technol 124(3):238–253

    Article  Google Scholar 

  • Watano S, Takashima H, Miyanami K (1997) Scale-up of agitation fluidized bed granulation by neural network. Chem Pharm Bull 45(7):1193–1197

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian School of Mines, Dhanbad, India

    Mohammad Nadeem & Haider Banka

  2. Department of Fuel and Mineral Engineering, Indian School of Mines, Dhanbad, India

    R. Venugopal

Authors
  1. Mohammad Nadeem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haider Banka
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Venugopal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Nadeem.

Ethics declarations

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.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nadeem, M., Banka, H. & Venugopal, R. A neural network-based approach for steady-state modelling and simulation of continuous balling process. Soft Comput 22, 873–887 (2018). https://doi.org/10.1007/s00500-016-2394-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-016-2394-5

Keywords

Search

Navigation