含时滞测量值下间歇过程的双维状态估计

基于粒子滤波研究了间歇过程的状态估计问题。根据间歇过程双维动态特性,针对关键参数在线检测精度低、离线分析时滞大等问题，分别建立一种双维状态转移模型和时滞测量模型，并利用贝叶斯方法及前/后向平滑，提出一种含时滞测量值下的双维状态估计算法。该算法通过融合先前批次和时滞测量值的信息提高估计精度，并且克服了离线采样周期和时滞时间不确定的问题。在数字仿真和啤酒发酵过程中的仿真应用验证了该算法的有效性。

Two dimensional state estimation in batch process with delayed measurements

This work investigates the state estimation in a batch process based on the particle filter method. Considering that the two dimensional dynamics and the key parameters are obtained online with low accuracy or offline with large time delay, a two dimensional state transition model and delayed measurement model are developed. In addition, a two dimensional state estimation algorithm is proposed by using Bayesian method and the forward-backward smoothing algorithm. The proposed algorithm improves the estimation accuracy by fusing information of previous batches and delayed measurements, and overcomes the influence of the uncertainty of offline sampling period and time delay. The applications in a numerical example and a beer fermentation show the effectiveness of the proposed method.