Prediction
of the laws of carbon steel erosion corrosion in sour water system based on decision tree and two kinds of
artificial neural network model
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Type
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Conference
Paper
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Author
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X.
Wu
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Author
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J.
Ren
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URL
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Pages
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3872-3876
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Date
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May
2016
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Conference
Name
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2016
Chinese Control and Decision Conference (CCDC)
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Abstract
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To
explore the corrosion induced failure problem of carbon steels common to
water system, researchers constructed an erosion failure database based on
self-built rotary erosion experimental device. They then devised a decision
tree based erosion level prediction model. In addition, by means of two
distinct neural networks, an erosion rate prediction model was designed based
on carbon steel erosion experimental samples.
Initially, self-organization mapping (SOM) network is applied to obtain the relevant relationship between variables by the explorative clustering analysis of multivariate samples. Then error back propagation (BP) neural network is adopted to model and predict corrosion rate of carbon steel samples. Test results revealed that the prediction accuracy of the decision tree model can be 100% and the average error the BP neural network model applied in this paper can be as low as 3.63%, offering a ovelw method for material selection and real time corrosion prediction and control in petrochemical system. |
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