Abstract
In this paper we report results for the prediction of thermo-dynamic properties based on neural networks, evolutionary algorithms and a combination of them. We compare backpropagation trained networks and evolution strategy trained networks with two physical models. Experimental data for the enthalpy of vaporization were taken from the literature in our investigation. The input information for both neural network and physical models consists of parameters describing the molecular structure of the molecules and the temperature. The results show the good ability of the neural networks to correlate and to predict the thermodynamic property. We also conclude that backpropagation training outperforms evolutionary training as well as simple hybrid training.
Acknowledgments:
The work presented is a result of the Collaborative Research Center SFB 531 sponsored by the Deutsche Forschungsgemeinschaft (DFG)
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References
T. Bäck. Evolutionary Algorithms in Theory and Practice. Oxford Univ. Press, New York, 1996.
L. M. Egolf and P. Jurs. Prediction of boiling points of organic heterocyclic compounds using regression and neural network techniques. In J. Chem. Inf. Comput. Sci. 33, pages 616–625. 1993.
H. Geyer, P. Ulbig, and S. Schulz. Encapsulated evolution strategies for the determination of group contribution parameters in order to predict thermodynamic properties. In 5th Int’l. Conf. on Parallel Problem Solving from Nature. Amsterdam, 1998.
K. Hornik, M. Stinchcombe, and H. White. Multilayer feedforward networks are universal approximators. Neural Networks, 2:359–366, 1989.
M. Mandischer. Evolving recurrent neural networks with non-binary encoding. In Proc. Second IEEE Int’l Conf. Evolutionary Computation (ICEC’ 95), vol. 2, pages 584–589, Perth, Australia, 1995. IEEE Press, Piscataway NJ.
D. E. Rummelhart and J. L. McClelland. PDP: Explorations in the Microstructure of Cognition, volume 1. MIT Press, Cambridge, MA, USA, 1986.
H.-P. Schwefel. Evolution and Optimum Seeking. Sixth-Generation Computer Technology. Wiley, New York, 1995.
P. Ulbig. Gruppenbeitragsmodelle UNIVAP & EBGCM. Dr.-Ing. Thesis, Univ. of Dortmund, Institute for Thermodynamics, 1996.
P. Ulbig, T. Friese, H. Geyer, C. Kracht, and S. Schulz. Prediction of thermodynamic properties for chemical engineering with the aid of Computational Intelligence. In Progress in Connectionist-Based Information Systems. Springer, 1997.
W. Wienholt. Minimizing the system error in feedforward neural networks with evolution strategy. In S. Gielen and B. Kappen, editors, Proc. of the Int’l. Conf. on Artificial Neural Networks, pages 490–493, London, 1993. Springer-Verlag.
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Mandischer, M., Geyer, H., Ulbig, P. (1999). Neural Networks and Evolutionary Algorithms for the Prediction of Thermodynamic Properties for Chemical Engineering. In: McKay, B., Yao, X., Newton, C.S., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1998. Lecture Notes in Computer Science(), vol 1585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48873-1_15
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DOI: https://doi.org/10.1007/3-540-48873-1_15
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