Data reconciliation and gross error detection: application in chemical processes

Abstract

Abstract. Measured data are normally corrupted by different kinds of errors in many chemical processes. In this work, a brief overview in data reconciliation and gross error detection believed as the most efficient technique in reducing the measurement errors and obtaining accurate information about the process is presented. In addition to defining the basic problem and a survey of recent developments in this area that is categorized in “Real Time Optimization” field, we will describe about advanced optimization methods in nonlinear cases. At the end, implementation of data reconciliation is illustrated on a challenging process of Claus as a case study and as a result, a modified and consistent model with regard to measured data is presented by simultaneous estimation of key model parameters. In our case study, automation capability of ASPEN HYSYS is used to provide interface environment to reach global optimum.