Neural network predictive control of a heat exchanger

The study attempts to show that using the neural network predictive control (NNPC) structure for control of thermal processes can lead to energy savings. The advantage of the NNPC is that it is not a linear-model-based strategy and the control input constraints are directly included into the synthesis. In the designed approach, the neural network is used as a non-linear process model to predict the future behaviour of the controlled process with distributed parameters. The predictive control strategy is used to calculate optimal control inputs. The efficiency of the described control approach is verified by simulation experiments and a tubular heat exchanger is chosen as a controlled process. The control objective is to keep the temperature of the heated outlet stream at a desired value and minimize the energy consumption. The NNPC of the heat exchanger is compared with classical PID control. Comparison of the simulation results obtained using NNPC and those obtained by classical PID control demonstrates the effectiveness and superiority of the NNPC because of smaller consumption of heating medium.

► Neural network predictive control is used for control of a tubular heat exchanger. ► The neural network represents a model of the non-linear process. ► The predictive control strategy calculates optimal control inputs. ► Neural network predictive control is compared with classical PID control. ► Neural network predictive control leads to smaller consumption of heating water.