Application of PLS and LS-SVM in difficult-to-measure process variable estimation

Very often important process variables which are concerned with the final product quality cannot be measured by a sensor or the measurements are too expensive and often not reliable. In order to enable continuous monitoring of process variables and efficient process control, soft-sensors are usually used to estimate these difficult-to-measure process variables. Soft-sensor is based upon mathematical model of the process. Process model building is based on plant data, taken from the process database. In this paper two methods, namely, Partial Least Squares (PLS) and Least Squares Support Vector Machines (LS-SVM), are used for difficult-to-measure process variables estimation. The methods are used for modeling simulated fluid storage process and oil distillation process. Results are compared and discussed. Advantages and disadvantages of each approach are outlined with respect to this specific application area. Additionally, hybridization of these methods is proposed which exploits good properties of both methods.