An adaptive data-driven modelling and optimization framework for complex chemical process design

Abstract

Current advances in computer-aided chemical process design and synthesis take advantage of surrogate modelling and superstructure optimization techniques. Conventionally, this is completed by using first-principle physical models or data-driven models to replace the original rigorous models for optimization and selection of a specific unit operation. Despite its achievements, this strategy is inefficient when dealing with complex process flowsheets such as utility and refrigeration systems where a large number of unit operations are heavily connected by recycling streams. To address this problem, an integrated data-driven modelling and optimization framework is proposed in this work. The framework first constructs a hybrid machine learning based surrogate model to automatically reduce the system dimensionality and capture the nonlinearity of the underlying chemical process. Then, an efficient optimization algorithm, in specific, evolutionary algorithm, is embedded to identify the optimal solution of this surrogate model. Quality and accuracy of the estimated optimal solution is finally validated against the rigorous process model. Through an iterative approach, optimal operating conditions for the entire process flowsheet are efficiently identified. Furthermore, the novel CryoMan Cascade cycle system for large scale liquefied natural gas manufacturing is used as the case study. This framework is demonstrated to be superior regarding time-efficiency, solution quality, and flexibility over the rigorous model based optimization approach.

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.