An efficient methodology to select high-performance M-estimators for robust data reconciliation

Abstract

Within the framework of Chemical Engineering, many exponential M-estimators have been proposed and tested. As usual, extensive simulation procedures are run to compare their performances. This work presents a deterministic methodology that reduces the computational endeavor of the comparative analysis. It becomes an effective technique for proposing and selecting high-performance M-estimators. The strategy uses the weight functions’ standardization to provide a common basis for the posterior application of Equivalent M-estimators and Tails Discrepancy definitions. In this way, identical M-estimators are figured out and those that behave likewise are identified. Whichever robust estimation procedure applied to estimate process variables and parameters can take advantage of the methodology results. For validation purposes, its conclusions are contrasted to the traditional performance measures obtained solving robust data reconciliation problems for two chemical processes.