Two-layer Dynamic EMPC of Organic Ranking Cycle systems with non-Gaussian disturbances

Abstract

Organic Rankine Cycle (ORC) is one of the energy recovery technologies that use low-boiling organics as the working fluid. During the control process of the ORC system, some random disturbances are often encountered, such as the mass flow of flue gas, the inlet temperature of the heat source, etc. The disturbance does not necessarily obey the Gaussian distribution. Based on the generalized correntropy (GC) criterion, this paper proposes a two-layer dynamic economic model predictive control (EMPC) method for Organic Ranking Cycle (ORC) systems with non-Gaussian disturbances. In the upper layer, the ratio of thermal efficiency to total heat transfer area is firstly used to establish an economic performance index function. Then, the real-time economically optimal trajectory, instead of the given fixed set-point, is calculated by dynamic EMPC, which uses the dynamic model of ORC instead of the steady-state model. In the lower layer, a GC-based MPC algorithm is used to make the system output track the reference trajectory obtained from the upper layer. Finally, the simulation results illustrate the effectiveness of the proposed GC-based two-layer EMPC method for ORC systems.