«Automation and Informatization of the fuel and energy complex»

Empirical models for automated process control systems: data clustering and verification, models robustness ensuring

UDC: 004.511.3
DOI: -

Authors:

¹ Ufa State Petroleum Technological University, Ufa, Russia

Keywords: reliability, data, sensitivity, regression, verification, situational modeling, non-stationary state, time series

Annotation:

The solution of problems of improved process control and safety at oil refining and petrochemical enterprises is based on the widespread use of models of information preparation and extraction, which are usually formed by processing statistical (empirical) data in the form of time series (TS). The problems of building of technological objects models, based on TS, are associated with the non-stationary of the object's characteristics as well as the nonlinearity of the relationships between the parameters. The prospects for solving the problems of modeling non-stationary objects lie in the way of situational modeling, when the TS is clustered into quasi-stationary fragments, each of which corresponds to a situational model. An important problem of models building is to ensure "roughness" (robustness, stability) to errors when determining the coefficients and measuring the input parameters of the models with high forecasting accuracy required for solving problems of control, diagnostics and data verification. The authors of the article consider approaches to analyzing the "roughness" of regression models for non-stationary objects and obtaining stable models based on TS processing.

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