Data-driven evolutionary optimisation for the design parameters of a chemical process: A case study

A significant challenge faced within the field of chemical plant design and optimisation is the uncertainty in the reaction parameter, indeterminate component failures and their impacts on the design and operation. Additionally, real-world data accumulated from these plants would contain stochastic elements that are difficult to model. To this end, stochastic and deterministic methods have been proposed to simulate the uncertainty and enable an understanding of the plant and how it may be optimised. Within the existing literature investigated, the optimisation is done under the assumption that the simulation (target function) is non-stochastic. We have found that the use of an Evolutionary Algorithm in the form of a Genetic Algorithm can find an optimal solution even when we allow the simulation to behave stochastically as it would in practical applications. Further, we note that the use of a surrogate Machine Learning model as a substitute for the stochastic simulation model leads to substantively improved solutions in significantly less time (1.82 times speedup). We argue that the use of Genetic Algorithms in the optimisation of chemical plant design, taking into account the stochastic nature of the plant and including indeterminate failures, is a worthwhile solution and that surrogate assisted evolutionary algorithms will improve this solution further.