Robust Data Reconciliation Applied to Steady State Model with Uncertainty

LLANOS, CLAUDIA ELIZABETH; SÁNCHEZ, MABEL CRISTINA

Communications in Computer and Information Science 1408

© Springer Nature Switzerland AG 2021

Año: 2021; p. 308 - 322

Abstract. Different researchers have proposed the treatment of uncertainties inmeasurements because they interfere in the process state estimation. Data reconciliationprocedure improves the information supplied bymeasurements, minimizingthe discrepancy existent between measurements and accurate process model. Thisproblem allows obtaining unbiased estimation whenmeasurements follow exactlya normal distribution. Nevertheless, the presence of outliers do not allow the useof the former procedure, therefore Robust Data Reconciliation is developed. Thislatter provides accurate solutions when measurements follow approximately thenormal distribution. Although many advances have been developed to treat measurementuncertainties in Data Reconciliation framework, there are not researchworks that consider model and measurement uncertainties simultaneously in presenceof outliers. In this work, a Simple robust Method, which takes advantage oftemporal redundancy, is applied to benchmarks that contain uncertain parameters.Performances measures are tested for different magnitudes of simulated outliersand compared with the ones provided by a classic Data Reconciliation procedure.Results show that the Robust Data Reconciliation procedure can yield unbiasedestimations of measurements and parameters when outliers and parametricuncertainties are present.