A Novel Soft Sensor Method Detecting Completion of Transition for Industrial Polymer Processes

AbstractSoft sensors are widely used to estimate values of process variables that are difficult to measure online, for example, polymer quality variables. Industrial polymer processes generally produce many grades of products. In order to reduce quantity of off-grade material and produce a consistent product, values of polymer quality variables should be estimated with high accuracy by using soft sensor models. However, the predictive accuracy during grade transition can be low because a state in a polymer reactor is unsteady in transition. Values of process variables in the unsteady state can differ from those which is used to construct a regression model. It is desired to know the time on which the polymer quality meets product specifications. Thus, we propose to construct a model which detects completion of transition in order to assure predicted values of the polymer quality variables after the transition. By using the model and constructing regression models for each grade of a product, values of the objective variables can be predicted with high accuracy, selecting a regression model appropriately. We analyzed real industrial data as application of the proposed method. The proposed method achieved higher predictive accuracy than traditional ones.