5. Conclusions

This chapter introduces a new refrigeration model and proposes a systematic methodology for operational optimization of multi-level refrigeration cycle. The methodology applies NLP model to minimize the overall power demand of the refrigeration cycle and uses GCC and linear refrigeration model that is based on regression from rigorous simulations. The GCC is used to obtain cooling duty of each level, while the linear refrigeration model is used to predict the actual coefficient performance of the complex refrigeration cycle that is decomposed into assembly simple cycles. The refrigeration model requires only condensing and evaporating temperatures. The effectiveness of the proposed optimization approach has been demonstrated on the case study of its application to ethylene cold-end process. The results of the case study demonstrate that the refrigeration model can predict the power demand within 10% of rigorous simulation. The optimization algorithm can find a close optimum solution within very short time (less than a second). Also, the results reveal that 9% saving in shaft power demand can be achieved by optimizing the operating conditions. The difference between the optimal operating conditions (i.e., the evaporation temperatures) found by GA and GRG is 1%. Although these findings support the validity of the refrigeration model, and the reliability and computational efficiency of the optimization approach in finding a close optimal solution, there are some factors that need to be considered in the future. These factors include the trade-offs between capital and operating costs, the opportunities for rejecting heat to a cold heat sink within the process rather than an external cooling utility, and the use of mixed refrigerant (the advantage of using mixed refrigerant can be explored by using a refrigeration database that includes the power demand at various operating conditions).
