A hybrid modeling approach for steady-state optimal operation of vapor compression refrigeration cycles

Abstract

This paper presents a steady-state hybrid modeling approach for vapor compression refrigeration cycles which is intended to achieve an optimal system operation from an energy consumption point of view. The model development is based on a static characterization of the main components of the cycle using a hybrid approach, and their integration in a new optimization block. This block allows to determine completely the system stationary state by means of a non-linear optimization procedure subjected to several constraints such as mechanical limitations, component interactions, environmental conditions and cooling load demand. The proposed method has been tested in an experimental pilot plant with good results. Model validation for each identified hybrid model is carried out from a set of experimental data of 82 stationary operating points, with prediction errors below ±10%. The model is also globally validated by comparing experimental and simulated data, with a global mean relative absolute error less than 5%. The basic control structure consists of three decentralized control loops where the controller variables are the secondary fluid temperature at the evaporator inlet, the superheat, and the condenser pressure. While the secondary temperature is assumed as an imposed requirement, the optimal set-points of the other two control loops are searched offline using the proposed refrigerant cycle model. This set-point optimality is defined according to the coefficient of performance for minimizing the total electrical power consumption of the system at steady-state. This energy saving has been confirmed experimentally. The proposed method can be easily adapted for different sets of controlled variables in case of modification of the basic control structure. Furthermore, other energy efficiency metrics can be handily adopted. Considering the tradeoff between the accuracy and computational cost of the hybrid models, the proposed procedure is expected to be used in real-time applications.

Control descentralizado adaptativo con PI por ganancia programada de un sistema de refrigeración por compresión de vapor

“…Uno de los principales problemas en la operación de los sistemas de refrigeración consiste en lograr las condiciones de operación deseadas mientras se minimiza el consumo de energía [8]. En el benchmark, se supone que la referencia del lazo de control de T e,sec,out no se puede modificar, ya que se considera un requisito de demanda impuesto.…”

Model-Based Condenser Fan Speed Optimization of Vapor Compression Systems

“…In this context, Scarpa et al [13] have stated that the most accurate models lead to a COP estimation error of ±5%, and ±10% can be considered as a high level of accuracy. In fact, the most models for set-point optimization perform a COP estimation accuracy of about ±10% [14][15][16][17][18][19][20]. The ideal condenser fan speed exhibits a strong dependence on the ambient temperature and the refrigeration capacity [7,8].…”

“…As a drawback of this method, a long time (over 50 min [7]) is required to drive the system to ideal operation. Zhao et al [10] and Ruz et al [17] presented steady-state optimization strategies based on the hybrid heat exchanger model suggested by Ding et al [25]. Zhao et al used a modified genetic algorithm to obtain optimum set-points, minimizing the total operating costs of the compressor, evaporator fan, and condenser fan.…”

Environmental Health Oriented Optimal Temperature Control for Refrigeration Systems Based on a Fruit Fly Intelligent Algorithm

“…With the development of the society and the urbanization process, refrigeration techniques are applied everywhere, which causes an acceleration of the growth of the carbon emissions around the world [3]. According to the surveys, almost 30% of the energy consumed all over the world is utilized for Heating, Ventilating, and Air Conditioning (HVAC) [4], while refrigerators account for about 28% of all the energy consumption of a typical US family [5].…”