Robust data reconciliation in a chemical reactor through simulated annealing optimization

Abstract

Robust data reconciliation (RDR) is an effective technique designed to minimize/annul gross errors drawbacks over estimated process variables. In the present work, a brief review on heuristic optimization methods devoted to RDR in chemical processes is performed. Twelve robust estimators were evaluated, including Smith and Bell estimators, which were never before used within this context. The performance of these estimators was evaluated in a Van de Vusse reaction system, described by a nonlinear system of equations. The problem was solved by both the simulated annealing method and the IPOPT framework. The results showed the efficiency and consistency of both schemes and, despite the relevance of gross errors, the redescending estimators exhibited better global performance.