Abstract
Process variables regularly control and evaluate industrial processes. Information with gross errors may in some cases not be attenuated by function reconciliation and change the calculation of process balance, leading optimization results towards non-feasible regions or to optimal sites. A promising alternative for reconciling functions is the use of robust functions. Current paper considers the above scenario and evaluates the fitness of some robust functions in solving in steady state chemical processes data reconciliation problems represented by linear and nonlinear systems in the presence of gross errors. Traditional Cauchy, Fair, Contaminated Normal and Logistic robust functions are used in the reconciliation problem where their estimates are compared to those obtained with the use of the latest features, such as New Target and Alarm. Rates for gross errors in tests were limited between 4 and 10σ of the measured current and elaborated a region of outliers. Results showed that New Target and Alarm functions are different from the others as the magnitude of the gross error increases, tending towards true rates specified by set point.
Highlights
Data reconciliation may be seen as a step towards improving the accuracy of data for use in modeling and optimization processes
Technology even when the estimated standard deviation of errors is different from the actual ones or when errors follow standard distribution (Jiang, Liu, & Li, 2014)
Robust functions have been studied by Huber and Ronchetti (2009) with the use of robust statistical tools to find solutions to problems that lacked the normal Gaussian distribution
Summary
Data reconciliation may be seen as a step towards improving the accuracy of data for use in modeling and optimization processes. According to technique precursors Kuehn and Davidson (1961), data reconciliation is a tool that, among a wide range of applications, allows optimum adjustment measures and estimates based on spatial redundancy and model conceived by conservational balances of mass and energy. Technology even when the estimated standard deviation of errors is different from the actual ones or when errors follow standard distribution (Jiang, Liu, & Li, 2014). Robust functions have been studied by Huber and Ronchetti (2009) with the use of robust statistical tools to find solutions to problems that lacked the normal Gaussian distribution. An important feature in the reconciliation procedure is the low sensitivity of these functions when the data
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