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.

Tom Savage

Applied Data-Driven Optimisation

I am a PhD student at Imperial College London & 2023 Enrichment student at the Alan Turing Institute. I have a background in Chemical Engineering and still enjoy teaching labs at Imperial College. Alongside my work in process systems engineering, I am affiliated with Winchester School of Art producing installations with the Tate on the intersection between AI and art. My interests include Bayesian optimisation, human-in-the-loop machine learning, cricket 🏏, and darts 🎯.

Dr. Ehecatl Antonio del Rio Chanona

Principal Investigator of OptiML

Antonio del Rio Chanona is the head of the Optimisation and Machine Learning for Process Systems Engineering group based in thee Department of Chemical Engineering, as well as the Centre for Process Systems Engineering at Imperial College London. His work is at the forefront of integrating advanced computer algorithms from optimization, machine learning, and reinforcement learning into engineering systems, with a particular focus on bioprocess control, optimization, and scale-up. Dr. del Rio Chanona earned his PhD from the Department of Chemical Engineering and Biotechnology at the University of Cambridge, where his outstanding research earned him the prestigious Danckwerts-Pergamon award for the best PhD dissertation of 2017. He completed his undergraduate studies at the National Autonomous University of Mexico (UNAM), which laid the foundation for his expertise in engineering.