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Evolutionary Computation in the Chemical Industry

  • Arthur Kordon
Part of the Studies in Computational Intelligence book series (SCI, volume 88)

Evolutionary computation has created significant value in the chemical industry by improving manufacturing processes and accelerating new product discovery. The key competitive advantages of evolutionary computation, based on industrial applications in the chemical industry are defined as: no a priori modeling assumptions, high quality empirical models, easy integration in existing industrial work processes, minimal training of the final user, and low total cost of development, deployment, and maintenance. An overview of the key technical, organizational, and political issues that need to be resolved for successful application of EC in industry is given in the chapter. Examples of successful application areas are: inferential sensors, empirical emulators of mechanistic models, accelerated new product development, complex process optimization, effective industrial design of experiments, and spectroscopy.

Keywords

evolutionary computation competitive advantage industrial applications chemical industry application issues of evolutionary computation 

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References

  1. Banzhaf, Wolfgang, Nordin, Peter, Keller, Robert E., and Francone, Frank D. (1998). Genetic Programming – An Introduction; On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, San Francisco, CA, USA.zbMATHGoogle Scholar
  2. Bharadwaj, Sundar G, Varadarajan, P. Rajan, and Fahy, John (1993). Sustainable competitive advantage in service industries: A conceptual model and research propositions. Journal of Marketing, 57(10):83–99.CrossRefGoogle Scholar
  3. Castillo, Flor, Kordon, Arthur, Sweeney, Jeff, and Zirk, Wayne (2004). Using genetic programming in industrial statistical model building. In O’Reilly, Una-May, Yu, Tina, Riolo, Rick L., and Worzel, Bill, editors, Genetic Programming Theory and Practice II, pages 31–48. Springer.Google Scholar
  4. Castillo, Flor A., Marshall, Ken A., Green, James L., and Kordon, Arthur K. (2002). Symbolic regression in design of experiments: A case study with linearizing transformations. In GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 1043–1047. Morgan Kaufmann Publishers.Google Scholar
  5. Christensen, Clayton M., Anthony, Scott D., and Roth, Erik A. (2004). Seeing What’s Next. Harvard Business School Press, Boston, MA.Google Scholar
  6. Engelbrecht, A. (2005). Fundamentals of Computational Swarm Intelligence,. Wiley, Chichester, UK.Google Scholar
  7. Breyfogel III, F. (2003). Implementing Six Sigma. Wiley, Hoboken, NJ, 2nd edition.Google Scholar
  8. Jain, L. and Martin, N., editors (1999). Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications. CRC Press, Boca Raton, FL.Google Scholar
  9. Jordaan, Elsa, Kordon, Arthur, Chiang, Leo, and Smits, Guido (2004). Robust inferential sensors based on ensemble of predictors generated by genetic programming. In Yao, Xin, Burke, Edmund, Lozano, Jose A., Smith, Jim, Merelo-Guervós, Juan J., Bullinaria, John A., Rowe, Jonathan, Kabán, Peter Tiňo Ata, and Schwefel, Hans-Paul, editors, Parallel Problem Solving from Nature - PPSN VIII, volume 3242 of LNCS, pages 522–531. Springer-Verlag.Google Scholar
  10. Katare, S., Kalos, A., and West, D. (2004). A hybrid swarm optimizer for efficient parameter estimation. In Proceedings of Congress of Evolutionary Computation, pages 309–315.Google Scholar
  11. Kordon, A., Kalos, A., and Adams, B. (2003a). Empirical emulators for process monitoring and optimization. In Proceedings of the IEEE 11th Conference on Control and Automation MED’2003.Google Scholar
  12. Kordon, A., Kalos, A., and Smits, G. (2001). Real time hybrid intelligent systems for automating operating discipline in manufacturing. In Artificial Intelligence in Manufacturing Workshop Proceedings of the 17th International Joint Conference on Artificial Intelligence IJCAI-2001, pages 81–87.Google Scholar
  13. Kordon, A., Smits, G., Kalos, A., and Jordaan, E. (2003b). Robust soft sensor development using genetic programming. In Nature-Inspired Methods in Chemometrics. Elsevier, Amsterdam.Google Scholar
  14. Kordon, Arthur, Castillo, Flor, Smits, Guido, and Kotanchek, Mark (2005). Application issues of genetic programming in industry. In Yu, Tina, Riolo, Rick L., and Worzel, Bill, editors, Genetic Programming Theory and Practice III, volume 9 of Genetic Programming, chapter 16, pages 241–258. Springer.Google Scholar
  15. Kordon, Arthur, Pham, Hoang, Bosnyak, Clive, Kotanchek, Mark, and Smits, Guido (2002). Accelerating industrial fundamental model building with symbolic regression: A case study with structure-property relationships. In Davis, Lawrence “Dave” and Roy, Rajkumar, editors, GECCO-2002 Presentations in the Evolutionary Computation in Industry Track, pages 111–116.Google Scholar
  16. Kordon, Arthur K. and Smits, Guido F. (2001). Soft sensor development using genetic programming. In Spector, Lee, Goodman, Erik D., Wu, Annie, Langdon, W. B., Voigt, Hans-Michael, Gen, Mitsuo, Sen, Sandip, Dorigo, Marco, Pezeshk, Shahram, Garzon, Max H., and Burke, Edmund, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 1346–1351. Morgan Kaufmann.Google Scholar
  17. Kotanchek, Mark, Kordon, Arthur, Smits, Guido, Castillo, Flor, Pell, R., Seasholtz, M. B., Chiang, L., Margl, P., Mercure, P. K., and Kalos, A. (2002). Evolutionary computing in Dow Chemical. In Davis, Lawrence “Dave” and Roy, Rajkumar, editors, GECCO-2002 Presentations in the Evolutionary Computation in Industry Track, pages 101–110, New York, New York.Google Scholar
  18. Leardi, R., Seasholtz, M. B., and Pell, R. (2002). Variable selection for multivariate calibration using a genetic algorithm: Prediction of additive concentrations in polymer films from fourier transforms-infrared spectral data. Analytica Chimica Acta, 461:522–531.CrossRefGoogle Scholar
  19. Parmee, I. (2001). Evolutionary and Adaptive Computing in Engineering Design. Springer, London, UK.Google Scholar
  20. Smits, Guido and Kotanchek, Mark (2004). Pareto-front exploitation in symbolic regression. In O’Reilly, Una-May, Yu, Tina, Riolo, Rick L., and Worzel, Bill, editors, Genetic Programming Theory and Practice II, chapter 17, pages 283–299. Springer.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Arthur Kordon
    • 1
  1. 1.The Dow Chemical CompanyFreeportUSA

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