Method and System for Modeling a Process in a Process Plant
First Claim
1. A method for generating a model of a process in a process plant, comprising:
- collecting a plurality of groups of data sets for the process in the process plant, the data sets generated from process variables of the process;
generating a plurality of regression models of the process using the groups of data sets, wherein each regression model is generated using a corresponding group of data sets from the plurality of groups, and wherein each regression model comprises a plurality of corresponding parameters generated using the corresponding group of data sets;
generating a composite model of the process to include the plurality of regression models;
generating a new model of the process using parameters of at least two of the regression models; and
revising the composite model of the process to replace the at least two of the regression models with the new model.
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Abstract
A system for detecting abnormal operation of at least a portion of a process plant includes a composite model for modeling at least the portion of the process plant. The model may be configurable to include multiple regression models corresponding to multiple different operating regions of the portion of the process plant. A new model may be generated from two or more of the regression models, and the composite model may be revised to replace the two or more regression models with the new model. The system may also include a deviation detector configured to determine if the actual operation of the portion of the process plant deviates significantly from the operation predicted by the composite model. If there is a significant deviation, this may indicate an abnormal operation.
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Citations
27 Claims
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1. A method for generating a model of a process in a process plant, comprising:
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collecting a plurality of groups of data sets for the process in the process plant, the data sets generated from process variables of the process; generating a plurality of regression models of the process using the groups of data sets, wherein each regression model is generated using a corresponding group of data sets from the plurality of groups, and wherein each regression model comprises a plurality of corresponding parameters generated using the corresponding group of data sets; generating a composite model of the process to include the plurality of regression models; generating a new model of the process using parameters of at least two of the regression models; and revising the composite model of the process to replace the at least two of the regression models with the new model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A tangible medium storing machine readable instructions, the machine readable instructions capable of causing one or more machines to:
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collect a plurality of groups of data sets for the process in the process plant, the data sets generated from process variables of the process; generate a plurality of regression models of the process using the groups of data sets, wherein each regression model is generated using a corresponding group of data sets from the plurality of groups, and wherein each model comprises a plurality of corresponding parameters generated using the corresponding group of data sets; generate a composite model of the process to include the plurality of regression models; generate a new model of the process using parameters of at least two of the regression models; and revise the composite model of the process to replace the at least two of the regression models with the new model.
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10. A method for generating a model of a process in a process plant, comprising:
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collecting data sets for the process in the process plant, the data sets generated from process variables of the process; generating a new regression model of the process using the collected data sets; revising a composite model of the process to include the new regression model in a plurality of regression models of the composite model; wherein each regression model in the plurality of regression models comprises a plurality of corresponding parameters generated using a corresponding group of data sets from a plurality of groups of data sets; determining whether a number N of regression models in the composite model exceeds a number NMAX; if the number N of regression models exceeds the number NMAX, generating a new model of the process using parameters of at least two of the regression models in the plurality of regression models; and revising the composite model of the process to replace the at least two of the regression models with the new model. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17)
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18. A tangible medium storing machine readable instructions, the machine readable instructions capable of causing one or more machines to:
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collect data sets for the process in the process plant, the data sets generated from process variables of the process; generate a new regression model of the process using the collected data sets; revise a composite model of the process to include the new regression model in a plurality of regression models of the composite model; wherein each regression model in the plurality of regression models comprises a plurality of corresponding parameters generated using a corresponding group of data sets from a plurality of groups of data sets; determine whether a number N of regression models in the composite model exceeds a number NMAX; if the number N of regression models exceeds the number NMAX, generate a new model of the process using parameters of at least two of the regression models in the plurality of regression models; and revise the composite model of the process to replace the at least two of the regression models with the new model.
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19. A system for detecting an abnormal operation in a process plant, comprising:
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a configurable composite model of the process in the process plant comprising a plurality of regression models, each regression model having been generated using a corresponding group of data sets from a plurality of groups of data sets, the data sets generated from process variables of the process, wherein each regression model comprises a plurality of corresponding parameters generated using the corresponding group of data sets, wherein the configurable composite model is capable of being subsequently configured to replace at least two of the regression models with a new model generated using parameters of the at least two of the regression models; and the system further comprising a deviation detector coupled to the configurable composite model, the deviation detector configured to determine if the process significantly deviates from an output of the composite model. - View Dependent Claims (20, 21, 22, 23, 24, 25, 26, 27)
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Specification