Hierarchical Neural Network Based Product Quality Prediction of Industrial Ethylene Pyrolysis Process
- Cite this paper as:
- Zhou Q., Xiong Z., Zhang J., Xu Y. (2006) Hierarchical Neural Network Based Product Quality Prediction of Industrial Ethylene Pyrolysis Process. 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
A two-layer hierarchical neural network is proposed to predict the product qualities of an industrial KTI GK-V ethylene pyrolysis process. The first layer of the model is used to classify these changes into different operating conditions. In the second layer, the process under each operating condition is modeled using bootstrap aggregated neural networks (BANN) with sequential training algorithm. The overall output is obtained by combining all the trained networks. Results of application to the actual process show that the proposed soft-sensing model possesses good generalization capability.
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