Model-based optimization for vapor compression refrigeration cycle

• Optimization problem is formulated based on hybrid models.
• A modified genetic algorithm together with a solution strategy is proposed.
• Simulation and experiment show 8.45% energy saving for work hours.

This paper presents a model-based optimization strategy for vapor compression refrigeration cycle. Through analyzing each component characteristics and interactions within the cycle, the optimization problem is formulated as minimizing the total operating cost of the energy consuming devices subject to the constraints of mechanical limitations, component interactions, environment conditions and cooling load demands. A MGA (modified genetic algorithm) together with a solution strategy for a group of nonlinear equations is proposed to obtain optimal set point under different operating conditions. Simulation studies are conducted to compare the proposed method with traditional on–off control strategy to evaluate its performance. Experiment results of a real practical system are also presented to demonstrate its feasibility.