Applications of Discrete-Event Simulation in the Chemical Industry

  • Sven Spieckermann
  • Mario Stobbe


Production processes in the chemical industry are to a large extend not discrete but continuous. Hence, the application of discrete-event simulation (DES) in this field is not as widespread as in discrete manufacturing. In order to apply DES methodology to chemical production processes, continuous aspects have to be covered sufficiently. This contribution briefly introduces and discusses combined discrete-continuous simulation approaches and illustrates the potential of the methodology using three cases of a leading German chemical company from supply chain optimization to the shop floor.


Supply Chain Winter Simulation Continuous Simulation Supply Chain Design Supply Chain Operation 
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  1. Alexander, C.W.: Disrete Event Simulation for Batch Processing. In: Perrone, L.F., Wieland, F.P., Liu, J., Lawson, B.G., Nicol, D.M., Fujimoto, R.M. (eds.) Proceedings of the 2008 Winter Simulation Conference, SCS International, San Diego, pp. 1929–1934 (2006)Google Scholar
  2. Bangsow, S.: Manufacturing Simulation with Plant Simulation and SimTalk. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. Barton, P.I., Pantelides, C.C.: Modeling of Combined Discrete/Continuous Processes. AIChE Journal 40(6), 966–979 (1994)CrossRefGoogle Scholar
  4. Bauer Jr., D.W., McMahon, M., Page, E.H.: An Approach for the Effective Utilization of GP-GPUS in Parallel Combined Simulation. In: Mason, S.J., Hill, R.R., Mönch, L., Rose, O., Jefferson, T., Fowler, J.W. (eds.) Proceedings of the 2008 Winter Simulation Conference, SCS International, San Diego, pp. 695–702 (2008)Google Scholar
  5. Cellier, F.E.: Combined Continuous/Discrete Simulation Applications, Techniques, and Tools. In: Wilson, J., Henriksen, J., Roberts, S. (eds.) Proceedings of the 1986 Winter Simulation Conference, pp. 24–33. ACM, New York (1986)Google Scholar
  6. Chen, E.J., Lee, Y.M., Selikson, P.L.: A Simulation Study of Logistics Activities in a Chemical Plant. Simulation Modelling Practice and Theory 10(3-4), 235–245 (2002)zbMATHCrossRefGoogle Scholar
  7. Fahrmann, D.A.: Combined Discrete Event Continuous Systems Simulation. Simulation 14(2), 61–72 (1970)CrossRefGoogle Scholar
  8. Günther, H.O., Yang, G.: Integration of Simulation and Optimization for Production Scheduling in the Chemical Industry. In: Proceedings of the 2nd International Simulation Conference, Malaga, Spain, pp. 205–209 (2004)Google Scholar
  9. ISA-S88. Batch Control Part 1: Models and Terminology. ANSI/ISA-88.01-1995, ISA, North Carolina, USA (1995) Google Scholar
  10. Jahangirian, M., Eldabi, T., Naseer, A., Stergioulas, L.K., Young, T.: Simulation in manufacturing and business: A review. European Journal of Operational Research 203, 1–13 (2010)CrossRefGoogle Scholar
  11. Mayer, G., Spieckermann, S.: Life-Cycle of Simulation Models: Requirements and Case Studies in the Automotive Industry. Journal of Simulation 4(4), 255–259 (2010)CrossRefGoogle Scholar
  12. Mehra, S., Inman, R.A., Tuite, G.: A simulation-based comparison of batch sizes in a continuous processing industry. Production Planning & Control 17(1), 54–66 (2006)CrossRefGoogle Scholar
  13. Mušič, G., Matko, D.: Simulation Support for Recipe Driven Process Operation. Computers & Chemical Engineering 22(suppl. 1), S887–S890 (1998)Google Scholar
  14. Schulz, M., Spieckermann, S.: Logistics Simulation in the Chemical Industry. In: Engell, S. (ed.) Logistic Optimization of Chemical Production Processes, pp. 21–36. Wiley, Chichester (2008)Google Scholar
  15. Sharda, B., Bury, S.J.: Bottleneck Analysis of Chemical Plant Using Discrete Event Simulation. In: Johansson, B., Jain, S., Montoya-Torres, J., Hugan, J., Yücesan, E. (eds.) Proceedings of the 2010 Winter Simulation Conference, SCS International, San Diego, pp. 1547–1555 (2010)Google Scholar
  16. Sharda, B., Vazquez, A.: Evaluating Capacity and Expansion Opportunities at Tank Farm: A Decision Support System Using Discrete Event Simulation. In: Rossetti, M.D., Hill, R.R., Johansson, B., Dunkin, A., Ingalls, R.G. (eds.) Proceedings of the 2008 Winter Simulation Conference, SCS International, San Diego, pp. 2218–2224 (2009)Google Scholar
  17. Smith, J.S.: Survey on the use of simulation for manufacturing system design and operation. Journal of Manufacturing Systems 22(2), 157–161 (2003)CrossRefGoogle Scholar
  18. Splanemann, R.: Production Simulation – A Strategic Tool to Enable Efficient Production Processes. Chemical Engineering & Technology 24(6), 571–573 (2001)CrossRefGoogle Scholar
  19. Stawinska, A. (ed.): European Business – Facts and Figures 2009 edition, Eurostat, Office for Official Publications of the European Communities (2009)Google Scholar
  20. Terzi, S., Cavalieri, S.: Simulation in the Supply Chain Context: A Survey. Computers in Industry 53(1), 3–16 (2004)CrossRefGoogle Scholar
  21. Watson, E.F.: An Application of Disrete-Event Simulation for Batch-Process Chemical -Plant Design. Interfaces 27(6), 35–50 (1997)CrossRefGoogle Scholar
  22. Wöllhaf, K., Fritz, M., Schulz, C., Engell, S.: BaSiP – Batch Process Simulation with Dynamically Reconfigured Process Dynamcis. Computers & Chemical Engineering 20(suppl. 2), S1281–S1286 (1996)CrossRefGoogle Scholar

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© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Sven Spieckermann
    • 1
  • Mario Stobbe
    • 2
  1. 1.SimPlan AGMaintalGermany
  2. 2.Evonik Industries AGMaintalGermany

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