Abstract
Applying data analysis-based intellectual optimization approach to the industrial process optimization may improve the weakness of the mechanical model-based optimization method which is mostly based on the incomplete knowledge and understanding of a system. Therefore, it shows great application prospects. In this paper, an operating parameter optimization model of crude oil distillation process is developed using BP neural network. Then genetic algorithm is applied to search for the optimal technological parameters. Simulation results indicate that this approach is effective and practical.
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References
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© 2004 Springer-Verlag Berlin Heidelberg
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Tang, H., Fan, Q., Xu, B., Wen, J. (2004). A Technological Parameter Optimization Approach in Crude Oil Distillation Process Based on Neural Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_138
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DOI: https://doi.org/10.1007/978-3-540-28648-6_138
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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