Surrogate Modelling and Optimization for Complex Liquefied Natural Gas Refrigeration Cycles

Abstract

In this paper, surrogate modelling and optimization is investigated for use in large scale chemical processes. A novel CryoMan cascade liquefied natural gas (LNG) refrigeration cycle is selected as the case study which has been highlighted for potential use within industry. Given its high nonlinearity and dimensionality (31 input variables and 20 output variables with a number of physical constraints) and short time horizon for real-time decision-making, an time-efficient optimization scheme must be developed to maximize process performance. Therefore, various supervised and unsupervised learning techniques as well as surrogate model structures are explored in order to accurately capture the behaviour of this highly complex and interrelated process flowsheet. Optimal solutions identified by the surrogate models are validated against the rigorous process model. Following from the challenges encountered by artificial neural network based surrogate models, Gaussian processes were adopted and combined with partial least squares to simultaneously reduce dimensionality and capture the nonlinearity of the underlying chemical process. Through this innovative surrogate modelling strategy, overall time to optimize the LNG production process was reduced by orders of magnitude compared to the rigorous model based optimization methodology, hence significantly facilitating the industrial application of this new process.