Multi-Objective Optimisation of Multistage Gas Phase Refrigeration Processes Using a Genetic Algorithm
Liquefied Natural Gas (LNG) is a clean burning fossil fuel which offers an energy density comparable to petrol and diesel fuels. It has been predicted that consumption of natural gas would increase by almost 70% from 2002 to 2025. There are many commercial processes available for the liquefaction of natural gas, for example, single mixed refrigeration (SMR), and cascade refrigeration. Another alternative is gas phase refrigeration processes. These processes are very flexible and inherently safer than condensing refrigeration processes. A shaftwork targeting method has recently been developed for multistage gas phase refrigeration systems. It has been found that the expansion/compression pressure ratio and the heat exchanger ΔTmin are the key parameters affecting energy efficiency.
In the present work, a multi-objective optimisation study has been carried out to optimise two different gas phase multistage refrigeration problems. The first is an idealised case of cooling a nitrogen gas stream and the second case is the liquefaction of natural gas. The objective functions for both optimisations include the capital cost and energy efficiency. For both problems a superstructure is used for the flowsheet simulation with multiple stages of refrigeration. A Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to generate the Pareto front where an extended range of process parameters are tested including the number of refrigeration stages.