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Non-linear chemical process modelling and application in epichlorhydrine production plant using wavelet networks

Lookup NU author(s): Dr Jie ZhangORCiD, Emeritus Professor Julian Morris


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A type of wavelet neural network, in which the scale function is dopted only, is proposed for non-linear dynamic process modelling. Its network size is decreased significantly and the weight coefficients can be estimated by a linear algorithm. The wavelet neural network holds some advantages superior to other types of neural networks. First, its network structure is easy to specify based on its theoretical analysis and intuition. Secondly, network training does not rely on stochastic gradient type techniques and avoids the problem of poor convergence or undesirable local minima. The excellent statistic properties of the weight parameter estimations can be proven here. Both theoretical analysis and simulation study show that the identification method is robust and reliable. Furthermore, a hybrid network structure incorporating first-principle knowledge and wavelet network is developed to solve a commonly existing problem in chemical production processes. Applications of the hybrid network to a practical production process demonstrates that model generalization capability is significantly improved.

Publication metadata

Author(s): Huang D, Jin Y, Zhang J, Morris AJ

Publication type: Article

Publication status: Published

Journal: Chinese Journal of Chemical Engineering

Year: 2002

Volume: 10

Issue: 4

Pages: 435-443

Print publication date: 01/08/2002

ISSN (print): 1004-9541

ISSN (electronic): 0892-0370

Publisher: Chemical Industry Press