A derivative‐free estimator was introduced to alleviate the drawbacks of the conventional Kalman filter when performing nonlinear analyses under different circumstances. In this work, the scaled Unscented Kalman Filter, Divided Difference Kalman filter, and Cubature Kalman filter (CKF) were selected to investigate the effectiveness of these filters in predicting the states of a complex semi‐batch reaction between propionic anhydride and 2‐butanol. The estimator's performance was evaluated under four different case studies, that is, under normal condition, under poor estimator initialization, under disturbances, and under parameter uncertainty. Results from the study show that CKF was the best option for an online dynamic optimization because of its highest degree of accuracy and stability under the normal and noisy conditions. Under normal condition, CKF yielded the lowest root mean square error of 0.61 × 10⁻². Under uncertain initial condition, disturbance and parameter uncertainty, the lowest error of 1.83 × 10⁻², 1.04 × 10⁻², and 0.81 × 10⁻² were obtained, respectively.

中文翻译：

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通过无导数估算器优化具有不确定性的自动催化

引入了无导数估计器，以减轻常规卡尔曼滤波器在不同情况下执行非线性分析时的缺点。在这项工作中，选择了无标度卡尔曼滤波器，除数卡尔曼滤波器和Cubature卡尔曼滤波器（CKF），以研究这些滤波器在预测丙酸酐和2-丁醇之间复杂的半间歇反应状态方面的有效性。在四个不同的案例研究中评估了估计器的性能，即在正常条件下，估计器初始化不佳，干扰和参数不确定性下。研究结果表明，CKF是在线动态优化的最佳选择，因为它在正常和嘈杂的条件下具有最高的准确性和稳定性。⁻²。下不确定初始条件，干扰和参数的不确定性，为1.83×10最低误差^-2，1.04×10 ^-2，和0.81×10 ^-2得到，分别。