Method and system of using inferential measurements for abnormal event detection in continuous industrial processes
First Claim
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1. An on-line system for calculating inferential estimators of key process conditions from normal process operating data of petro-chemical and industrial processes comprising:
- a) real-time data collector,b) real-time data preprocessor,c) steady state and/or dynamic inferential models wherein said models estimate values for the current quality value (Yestimate) and predict abnormal values for the future quality value (Ypredicted), and the inputs to said inferential models for calculating (Ypredicted) are not time synchronized,d) statistical significance calculator of estimates of the models,e) means for summarizing multiple model results, andf) predictive displays for future abnormal events of said continuous petro-chemical and industrial process, andg) diagnostic displays for event analysis.
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Abstract
The present invention is a method and system for detecting an abnormal on-line analysis or laboratory measurement and for predicting an abnormal quality excursion due to an abnormal process condition.
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28 Claims
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1. An on-line system for calculating inferential estimators of key process conditions from normal process operating data of petro-chemical and industrial processes comprising:
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a) real-time data collector, b) real-time data preprocessor, c) steady state and/or dynamic inferential models wherein said models estimate values for the current quality value (Yestimate) and predict abnormal values for the future quality value (Ypredicted), and the inputs to said inferential models for calculating (Ypredicted) are not time synchronized, d) statistical significance calculator of estimates of the models, e) means for summarizing multiple model results, and f) predictive displays for future abnormal events of said continuous petro-chemical and industrial process, and g) diagnostic displays for event analysis. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28)
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