A novel framework for integrating data mining with control loop performance assessment

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dc.contributor.author Das, Laya
dc.contributor.author Srinivasan, Babji
dc.contributor.author Rengaswamy, Raghunathan
dc.date.accessioned 2015-09-19T09:48:36Z
dc.date.available 2015-09-19T09:48:36Z
dc.date.issued 2016-01
dc.identifier.citation Das, Laya; Srinivasan, Babji and Rengaswamy, Raghunathan, “A novel framework for integrating data mining with control loop performance assessment”, AIChE Journal, DOI: 10.1002/aic.15042, vol. 62, no. 1, pp. 146-165, Jan. 2016.
dc.identifier.issn 00011541
dc.identifier.uri dx.doi.org/10.1002/aic.15042
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/1928
dc.description.abstract Data driven control loop performance assessment techniques assume that the data being analysed correspond to single plant-controller configuration. However in an industrial setting where processes are affected due to the presence of feedstock variability and drifts, the plant-controller configuration changes with time. Also, user-defined benchmarking of control loops (common in industrial plants) requires that the data corresponding to optimal operation of the controller be known. However such information might not be available beforehand in which case it is necessary to extract the same from routine plant operating data. We propose a technique that addresses these fundamental requirements for ensuring reliable performance assessment. The proposed technique performs a recursive binary segmentation of the data and makes use of the fact that changes in controller settings translate to variations in plant output for identifying regions corresponding to single plant-controller configurations. The statistical properties of the data in each such window are then compared with the theoretically expected behaviour to extract the data corresponding to optimal configuration. This approach has been applied on: (i) raw plant output (ii) Hurst exponent and (iii) minimum variance index of the process data. Simulation examples demonstrate the applicability of proposed approach in industrial settings. A comparison of the three routes is provided with regard to the amount of data needed and the efficacy achieved. Key results are emphasised and a framework for applying this technique is described. This tool is of significance to industries interested in an automated analysis of large scale control loop data for multiple process variables that is otherwise left un-utilised. This article is protected by copyright. All rights reserved. en_US
dc.description.statementofresponsibility by Laya Das, Babji Srinivasan and Raghunathan Rengaswamy
dc.format.extent vol. 62, no. 1, pp. 146-165
dc.language.iso en_US en_US
dc.publisher Wiley Online Library en_US
dc.subject Control loop performance assessment en_US
dc.subject Minimum Variance Index en_US
dc.subject Hurst Exponent;Interval halving en_US
dc.subject Non-stationary data analysis en_US
dc.title A novel framework for integrating data mining with control loop performance assessment en_US
dc.type Article en_US
dc.relation.journal AIChE Journal


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