Enhancing the performance of a parallel nitrogen expansion liquefaction process (NELP) using the multi-objective particle swarm optimization (MOPSO) algorithm

Because of the complex relationship of the characteristics of the liquefied natural gas (LNG) processes, the use of multi-objective optimization algorithms seems rational, for the reason that these methods have the ability to tradeoff between different objectives. The unit energy consumption (UEC); the sizes of the LNG heat exchangers; and the liquefaction rate (LR) are chosen as the objective functions of the MOPSO algorithm. Optimizations were performed under two separate circumstances: initially, the optimization in the early stages of the plant design was considered; and consequently the optimization in the operational conditions was implemented. A fuzzy-based clustering method is then used to choose the optimum solution from the resulting points of the Pareto fronts derived from the MOPSO algorithm. The performance of this optimization is compared against the performances of a base case and the optimization performed on the same process using the genetic algorithm (GA). The utilized algorithm improves the objectives values, reduces the exergy destructions in the process and enriches the performance of the LNG heat exchangers. In addition, it is confirmed that the optimization performed at the early stages of the process design yields better results and cost savings. (C) 2019 Elsevier Ltd. All rights reserved.