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.

稳健的数据协调是一种有效的技术，旨在最大程度地减少与估计的过程变量相比造成的严重错误缺陷。这项工作针对化学反应器问题进行了综述，由于强烈的非线性约束，化学反应器问题产生了具有挑战性的场景，尚未针对几种稳健的估计量进行比较。主要贡献是对16种鲁棒估计量进行比较分析，包括自19世纪史密斯（Smith）估计量以来一直发展到新的Jin，Correntropy和Xie。在稳态条件下的三个案例研究中对这些估计器的性能进行了分析，包括带有Van de Vusse反应系统的非等温CSTR反应器。它使用IPOPT和在Scilab软件中实现的模拟退火优化器。