Model predictive control of long Transfer-line cooling process based on Back-Propagation neural network

Abstract

• A model predictive control (MPC) method based on back-propagation (BP) neural network as a surrogate inversion model was designed to improve the automation of the long transfer-line pre-cooling process. • A one-dimensional simulation model based on unsteady flow and heat transfer process was used to generate training datasets for BP neural networks, and reasonable cleaning rules are the key to obtain better training results. • Simulation and test results show that the MPC method can be applied to the automatic control of nonlinear large hysteresis dynamical systems such as the long transfer-line pre-cooling process. As the scale of large cryogenic systems continues to expand, the thermal inertia and nonlinear characteristics of the pre-cooling process of long-distance cryogenic transfer-line become obvious, and the traditional control methods are less effective in controlling such nonlinear large hysteresis time-varying systems. To improve the automation of the pre-cooling process, a Model Predictive Control (MPC) method based on Back-Propagation (BP) neural network as a surrogate inversion model was designed and deployed on a large helium cryogenic system of the Platform of Advanced Photon Source (PAPS). Simulation and test results show that the MPC method can be applied to the automatic control of nonlinear large hysteresis dynamical systems; the BP neural network as a surrogate model can invert the one-dimensional flow heat transfer model better. The actual test results on the PAPS cryogenic system show that the method can realize the automatic pre-cooling of long transfer-line, and the overall cooling effect is stable and efficient, with the maximum absolute temperature difference of no more than 3.2 K and the maximum relative temperature difference of no more than 2.1% from the ideal cooling line.