NDLI: Estimation of impurity and fouling in batch polymerisation reactors using stacked neural networks

Content Provider	IEEE Xplore Digital Library
Author	Zhang, J. Morris, A.J. Martin, E.B. Kiparissides, C.
Copyright Year	1997
Description	Author affiliation: Dept. of Chem. & Process Eng., Newcastle upon Tyne Univ., UK (Zhang, J.)
Abstract	A robust method for the estimation of reactive impurities and reactor fouling during the early stage of batch polymerisation using stacked neural networks is reported. Data for building neural network models are resampled using the bootstrap re-sampling technique to form several sets of training data. For each set of training data, a neural network model is developed. Predictions from individual networks are combined to form the final model prediction in order to improve model accuracy and robustness. A further benefit of bootstrap aggregated neural network is that confidence bounds for model predictions can be formulated. Stacked neural networks are used to build an inverse model of the reactor. The amounts of impurities and fouling can be worked out by comparing the predicted effective initial reaction conditions with the nominal initial conditions. The proposed techniques have been successfully applied to a pilot scale batch methyl methacrylate polymerisation reactor.
Starting Page	247
Ending Page	251
File Size	530853
Page Count	5
File Format	PDF
ISBN	0780338324
ISSN	07431619
DOI	10.1109/ACC.1997.611795
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-06-06
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Impurities Intelligent networks Polymers Inductors Neural networks Predictive models Robustness Training data Chemical analysis Chemical engineering
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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