Abstract

The vapor compression refrigeration cycle is the most common method used for removing heat from a lower temperature level to a higher temperature level using a mechanical work. At lower temperatures (typically lower than 40C), complex refrigeration schemes, such as cascaded refrigeration cycles, may be needed, increasing the complexity of the models used to predict the power requirements. This chapter introduces a new linear refrigeration model to predict the shaft power demand of the refrigeration cycle given the cooling demand, the condensing, and evaporation temperatures. The refrigeration model is based on regression of rigorous simulation results. This chapter also proposes a new systematic optimization method for minimizing the work consumed in refrigeration system. The methodology employs nonlinear model to find the optimum refrigeration temperature levels and their cooling duties. To solve the nonlinear problem, generalized reduced gradient algorithm is used. A case study is presented to demonstrate the advantage using of the proposed methodology. Results show that the difference in power prediction between rigorous simulation and the new refrigeration model is about 10%. The work consumed in refrigeration cycle can be reduced to 9% compared to the base case when the operating conditions are optimized.

Keywords: The coefficient of performance, multi-stage cycle, cascade cycle, an ethylene cold-end process
