Abstract
It’s very time-consuming to evaluate thermal properties by rigorous methods in process real-time simulation, especially when the simulated project relates to multi-units and multi-components, which takes about 70 to 80 percent of the total simulation time. We developed a new reduced method for thermal properties evaluation based on the artificial neural net(ANN), in which we established several reduced evaluation models using ANN, such as models of vapor-liquid equilibrium, models of vapor-liquid enthalpy and models of temperature calculated from given enthalpy. We used the reduced models in a dynamic distillation simulation. Compared with rigorous thermal properties models, the ANN-reduced models could save 10 to 20 times simulation time with a satisfied accuracy. The results show it’s an efficient and effective method.
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© 2006 Springer-Verlag Berlin Heidelberg
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Yang, X., Bi, R., Li, Y., Zheng, S. (2006). Thermal Properties Reduced Models by ANN in Process Simulation. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_155
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DOI: https://doi.org/10.1007/11760191_155
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
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