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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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.
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.
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.
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.
Christensen, Clayton M., Anthony, Scott D., and Roth, Erik A. (2004). Seeing What’s Next. Harvard Business School Press, Boston, MA.
Engelbrecht, A. (2005). Fundamentals of Computational Swarm Intelligence,. Wiley, Chichester, UK.
Breyfogel III, F. (2003). Implementing Six Sigma. Wiley, Hoboken, NJ, 2nd edition.
Jain, L. and Martin, N., editors (1999). Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications. CRC Press, Boca Raton, FL.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Parmee, I. (2001). Evolutionary and Adaptive Computing in Engineering Design. Springer, London, UK.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kordon, A. (2008). Evolutionary Computation in the Chemical Industry. In: Yu, T., Davis, L., Baydar, C., Roy, R. (eds) Evolutionary Computation in Practice. Studies in Computational Intelligence, vol 88. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75771-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-75771-9_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75770-2
Online ISBN: 978-3-540-75771-9
eBook Packages: EngineeringEngineering (R0)