Zhang Zhenhui, Zhang Zhengjiang, Hong Zhihui
National-Local Joint Engineering Laboratory for Digitalize Electrical Design Technology, College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, China.
Polymers (Basel). 2022 Feb 28;14(5):973. doi: 10.3390/polym14050973.
The free radical polymerization of styrene (FRPS) is a complex process system with uncertain parameters in its mechanistic model. When the reaction conditions are switched, or the reaction process generates faults, the parameters will change. Therefore, state and parameter estimation (SPE) becomes an important part of the process monitoring and process control for free radical polymerization of styrene. The unscented Kalman filter (UKF) is widely used for nonlinear process systems, but it rarely considers the problem of model parameter uncertainty. UKF can be used for SPE, called UKF-based SPE (UKF-SPE), where the parameters are usually estimated simultaneously as an extension of the state space. However, when the parameters change with system switching, the traditional UKF-SPE cannot detect and track the parameter changes in time, and inaccurate parameters generate modeling errors. To deal with the problem, a UKF-based robust SPE method (UKF-RSPE) for the free radical polymerization of styrene with variable parameters is proposed, introducing a parameter testing criterion based on hypothesis testing and moving windows to directly detect whether the parameters have changed. Based on the detection results, a gradient descent method with adaptive learning rate is used to iteratively update the parameters to speed up the tracking of the parameters and to obtain more accurate parameters and states. Finally, the proposed UKF-based robust SPE is applied to free radical polymerization of styrene in a jacketed continuous stirred tank reactor. The experimental results verify the effectiveness and robustness of the method, which can track the parameters faster and obtain more accurate states.
苯乙烯的自由基聚合（FRPS）是一个复杂的过程系统，其机理模型中的参数具有不确定性。当反应条件切换或反应过程出现故障时，参数会发生变化。因此，状态和参数估计（SPE）成为苯乙烯自由基聚合过程监测和过程控制的重要组成部分。无迹卡尔曼滤波器（UKF）广泛应用于非线性过程系统，但很少考虑模型参数不确定性问题。UKF可用于SPE，称为基于UKF的SPE（UKF-SPE），其中参数通常作为状态空间的扩展同时进行估计。然而，当参数随系统切换而变化时，传统的UKF-SPE无法及时检测和跟踪参数变化，参数不准确会产生建模误差。为解决该问题，提出一种用于变参数苯乙烯自由基聚合的基于UKF的鲁棒SPE方法（UKF-RSPE），引入基于假设检验和移动窗口的参数测试准则直接检测参数是否发生变化。基于检测结果，使用具有自适应学习率的梯度下降法迭代更新参数，以加快对参数的跟踪并获得更准确的参数和状态。最后，将所提出的基于UKF的鲁棒SPE应用于夹套连续搅拌釜式反应器中苯乙烯的自由基聚合。实验结果验证了该方法的有效性和鲁棒性，其能够更快地跟踪参数并获得更准确的状态。
