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.
- Neural Networks
- Evolution Strategies
- Chemical Engineering
The work presented is a result of the Collaborative Research Center SFB 531 sponsored by the Deutsche Forschungsgemeinschaft (DFG)
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© 1999 Springer-Verlag Berlin Heidelberg
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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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