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Inferential estimation of polymer quality using bootstrap aggregated neural networks

Lookup NU author(s): Dr Jie ZhangORCiD


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Inferential estimation of polymer quality in a batch polymerisation reactor using bootstrap aggregated neural networks is studied in this paper. Number average molecular weight and weight average molecular weight are estimated from the on-line measurements of reactor temperature, jacket inlet and outlet temperatures, coolant flow rate through the jacket, monomer conversion, and the initial batch conditions. Bootstrap aggregated neural networks are used to enhance the accuracy and robustness of neural network models built from a limited amount of training data. The training data set is re-sampled using bootstrap re-sampling with replacement to form several sets of training data. For each set of training data, a neural network model is developed. The individual neural networks are then combined together to form a bootstrap aggregated neural network. Determination of appropriate weights for combining individual networks using principal component regression is proposed in this paper. Confidence bounds for neural network predictions can also be obtained using the bootstrapping technique. The techniques have been successfully applied to the simulation of a batch methyl methacrylate polymerisation reactor.

Publication metadata

Author(s): Zhang J

Publication type: Article

Publication status: Published

Journal: Neural Networks

Year: 1999

Volume: 12

Issue: 6

Pages: 927-938

Print publication date: 01/07/1999

ISSN (print): 0893-6080

ISSN (electronic): 1879-2782

Publisher: Pergamon


DOI: 10.1016/S0893-6080(99)00037-4


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