Support vector regression model based predictive control of water level of U-tube steam generators

•Water level of U-tube steam generators was controlled in a model predictive fashion.•Models for steam generator water level were built using support vector regression.•Cost function minimization for future optimal controls was performed by using the steepest descent method.•The results indicated the feasibility of the proposed method. A predictive control algorithm using support vector regression based models was proposed for controlling the water level of U-tube steam generators of pressurized water reactors. Steam generator data were obtained using a transfer function model of U-tube steam generators. Support vector regression based models were built using a time series type model structure for five different operating powers. Feedwater flow controls were calculated by minimizing a cost function that includes the level error, the feedwater change and the mismatch between feedwater and steam flow rates. Proposed algorithm was applied for a scenario consisting of a level setpoint change and a steam flow disturbance. The results showed that steam generator level can be controlled at all powers effectively by the proposed method.