  Chemical plant design and optimisation have proven challenging due to the
complexity of these real-world systems. The resulting complexity translates
into high computational costs for these systems' mathematical formulations and
simulation models. Research has illustrated the benefits of using machine
learning surrogate models as substitutes for computationally expensive models
during optimisation. This paper extends recent research into optimising
chemical plant design and operation. The study further explores Surrogate
Assisted Genetic Algorithms (SA-GA) in more complex variants of the original
plant design and optimisation problems, such as the inclusion of parallel and
feedback components. The novel extension to the original algorithm proposed in
this study, Surrogate Assisted NSGA-\Romannum{2} (SA-NSGA), was tested on a
popular literature case, the Pressure Swing Adsorption (PSA) system. We further
provide extensive experimentation, comparing various meta-heuristic
optimisation techniques and numerous machine learning models as surrogates. The
results for both sets of systems illustrate the benefits of using Genetic
Algorithms as an optimisation framework for complex chemical plant system
design and optimisation for both single and multi-objective scenarios. We
confirm that Random Forest surrogate assisted Evolutionary Algorithms can be
scaled to increasingly complex chemical systems with parallel and feedback
components. We further find that combining a Genetic Algorithm framework with
Machine Learning Surrogate models as a substitute for long-running simulation
models yields significant computational efficiency improvements, 1.7 - 1.84
times speedup for the increased complexity examples and a 2.7 times speedup for
the Pressure Swing Adsorption system.
