Neural Koopman operator-assisted model predictive control of an Organic Rankine Cycle

Abstract

This study deals with the Neural Koopman operator-assisted model predictive control of an Organic Rankine Cycle (ORC). The modeling of the evaporator and condenser is accomplished by utilizing the Finite Control Volume (FCV) method, and the modeling of the turbine and pump is fulfilled with pseudo-steady state equations. Afterward, the identification of the neural Koopman model is carried out with the input–output data obtained from the developed ORC model. The identification process resulted in 0.01121 and 0.01119 training and validation losses, respectively, which are sufficiently acceptable values. A linear MPC design is proposed to control the system in the Koopman-identified linear state space. Four different controllers are designed, each having different objectives, and their performances are compared with each other. The results reveal that the second law controller comes up with the most desirable exergy destruction rate of 2349 W, which is 1.44 % lower than the worst performer entropy generation controller. Moreover, the first and second law controllers result in the highest overall first and second law efficiencies, 0.14258 and 0.42945, respectively, through the simulation time. It is realized that the performance of the controllers is in line with their assigned objectives, and the proposed controller effectively regulates the system.