Using evolutionary search to optimise the energy consumption for natural gas liquefaction

Liquefaction of natural gas is a highly energy intensive process. Therefore its energy optimisation is an important matter. Sequential Quadratic Programming (SQP) will often be the preferred method for solving these optimisation problems, but there are some obstacles due to the fact that they use the local shape of the functions. Evolutionary search could be a way of getting around these problems and in this work, evolutionary search methods were combined and adapted to the liquefaction problem. The research focused on how to efficiently use all the function evaluations to obtain robust convergence, leading to the concept of diversity; and secondly how to deal with the infeasible individuals. Tests were performed on a benchmark function to assess the effect of different methods from the literature and the parameters which control them. Finally an application to the process simulator showed satisfactory results which were less than 5% from the assumed optimal solution.

► Evolutionary search handles the complexity of the liquefaction process optimisation.
► Maintaining diversity in the population improves the robustness of the algorithm.
► The use of information contained in the infeasible individuals helps convergence.