Correntropy based data reconciliation and gross error detection and identification for nonlinear dynamic processes

• Correntropy based nonlinear dynamic data reconciliation (CNDDR) is proposed.
• CNDDR can effectively decrease the influence of large measurement errors.
• A combined strategy is used to detect and identify different types of gross errors.
• The effectiveness is shown via the simulation results in a polymerization process.

Measurement information in dynamic chemical processes is subject to corruption. Although nonlinear dynamic data reconciliation (NDDR) utilizes enhanced simultaneous optimization and solution techniques associated with a finite calculation horizon, it is still affected by different types of gross errors. In this paper, two algorithms of data processing, including correntropy based NDDR (CNDDR) as well as gross error detection and identification (GEDI), are developed to improve the quality of the data measurements. CNDDR's reconciliation and estimation are accurate in spite of the presence of gross errors. In addition to CNDDR, GEDI with a hypothesis testing and a distance–time step criterion identifies types of gross errors in dynamic systems. Through a case study of the free radical polymerization of styrene in a complex nonlinear dynamic chemical process, CNDDR greatly decreases the influence of the gross errors on the reconciled results and GEDI successfully classifies the types of gross errors of the measured data.