Abstract
Mathematical modelling in paper industry is to a large extent based on physical models that usually contain empirical correlations and assumptions. Milosavljevic and Heikkilå [1] derived a mathematical model for a counter-flow wet cooling tower based on one-dimensional heat and mass balance equations using an expression for the volumetriv heat transfer coefficient. In the present study, volumetric heat transfer coefficient values determined by the above-mentioned model on the basis of measurements from a pilot cooling tower were approximated with feed-forward neural networks. It was found that a considerably more accurate approximation of the coefficient was obtained with the neural network than by conventional non-linear regression. The implementation of the expression in the first-principles model can lead to more accurate dimensioning of cooling towers, which results in lower equipment costs and better energy utilization.
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References
Milosavljevic, N. and Heikkilä, P.: A Comprehensive Approach to Cooling Tower Design. To appear in Applied Thermal Engineering, (2001).
Fredman, T. and Saxen H.: Modelling and Simulation of a Cooling Tower. Proceedings of European Simulation Multiconference, 66–70, (1995).
Levenberg, K.: A Method for Solution of Certain Nonlinear Problems in Least-Squares. Quart. Appl. Math. 2, 164–168, (1944).
Marquardt, D.W.: An algorithm for least-squares estimation of nonlinear parameters, J. SIAM. 11, 431–441, (1963).
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© 2001 Springer-Verlag Wien
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Milosavljevic, N., Saxén, H. (2001). Hybrid Model of Cooling Tower Based on First Principles and Neural Networks. In: Kůrková, V., Neruda, R., Kárný, M., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6230-9_97
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DOI: https://doi.org/10.1007/978-3-7091-6230-9_97
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83651-4
Online ISBN: 978-3-7091-6230-9
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