Nonlinear Identification of the Suction Manifold of a Supermarket Refrigeration System Using Wavelet Networks

The dynamics of the suction manifold of a high-fidelity simulation benchmark model of a modified supermarket refrigeration system created in MATLAB 2024a and Simulink 2024a is modeled using a nonlinear system identification technique. The original model consists of a cold storage room, three open display cases, the suction manifold, and the compressor rack. Since open display cases are less energy-efficient, they were removed, while the cold storage room with a door was used for simulation. The suction manifold model has two outputs: the suction pressure and the compressor’s power consumption; and it has three inputs: the mass flow of refrigerant, the ambient temperature, and the compressor capacity. A fourteen-day simulation was carried out, and synthetic data were generated from the input and output data of the simulation model. These data were divided into estimation data and validation data. Wavelet networks were then utilized to estimate and validate a nonlinear ARX model. The comparison between the estimation data and the validation data shows a goodness of fit of 87.8% for the suction pressure and 100% for the compressor power, with a simulation focus. The 100% fit for the compressor power occurred because wavelet networks provide excellent identification for nonlinear static systems and the compressor power response was based on static modeling assumption while the suction pressure response was based on dynamic modeling assumption. The data-driven identified model of the suction manifold was stable and robust and could handle strong nonlinearities of the input and output variables when used to replace the Simulink model of the suction manifold subsystem in the simulation benchmark. The simulation results clearly demonstrate how complex refrigeration system subsystems can be replaced with simpler and data-compliant data-driven models.