Characteristic Identification of Flow Control Valves Based on Data-Model-Fusion in Actual Industrial Scenarios

Control valves are important actuators in industrial processes. However, valve models identified by traditional system identification methods are difficult to work in actual industrial scenarios due to the influence of dynamic working conditions and interference, which also limits in-depth research on monitoring, diagnosis, control, etc. of control valves. To solve this issue, this article proposes a flow control valve identification method based on the idea of model-data-fusion. The proposed method can maintain good flow estimation performance by determining the relationship between the sensor data and the flow control valve model, even in case of changes in operating conditions. Meanwhile, a window down sampling (WDS) method is proposed to assist the modeling and testing process. On the basis of WDS, multiple flow characteristic models are established, and the performance of different models is evaluated based on the principle of feature selection. Furthermore, the convergence of the proposed parameter optimization method is proved in this study. The proposed flow estimation method is verified by the actual industrial data of a chemical plant.