Robust data reconciliation in chemical reactors

Robust data reconciliation is an effective technique designed to minimize gross errors drawbacks over estimated process variables. This work presents a review focusing on chemical reactor problems, which generate a challenging scenario due to strongly nonlinear constraints and have not been compared in terms of several robust estimators. The main contribution is to present a comparative analysis of 16 robust estimators, including since Smith estimator, developed in the XIX century, until the newly Jin, Correntropy and Xie ones. The performance of these estimators was analyzed in three case studies under steady-state conditions, including a non-isothermal CSTR reactor with a Van de Vusse reaction system. It was used IPOPT and simulated annealing optimizers implemented in Scilab software. The results showed the efficiency and consistency of the implemented methods, and although the influence of gross errors was significant, the redescending type estimators like Correntropy, Xie and Biweight showed a better overall performance. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This study reviewed chemical reactor problems and compared the efficiency of 16 robust estimators, finding that improved estimators like Correntropy, Xie, and Biweight showed better performance.