Model-based optimization for vapor compression refrigeration cycle

• Published in: Energy (Oxford) Vol. 55; pp. 392 - 402
• Main Authors: Zhao, Lei, Cai, Wenjian, Ding, Xudong, Chang, Weichung
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 15.06.2013; Elsevier
• Subjects: algorithms; Applied sciences; Compressing; cooling; Cooling loads; Energy; energy costs; Energy. Thermal use of fuels; equations; Exact sciences and technology; Feasibility; Global optimization; Hybrid components models; Modified genetic algorithm; Nonlinear equations; Operating costs; Optimization; Refrigerating engineering; Refrigerating engineering. Cryogenics. Food conservation; Refrigeration; Strategy; system optimization; System simulation and testing; Techniques. Materials; Vapor compression refrigeration cycle; vapors; Global optimization; Hybrid components models; System simulation and testing; Modified genetic algorithm; Vapor compression refrigeration cycle; Vapor compression; Refrigeration cycle; Models; Modeling; Optimization; Compression refrigeration
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2013.02.071

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. •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.