Abstract
In recent years, Multi-Objective Optimisation (MOO) has increasingly been applied in chemical engineering problems where multiple, often conflicting, objectives need to be optimized simultaneously. Genetic Evolutionary Algorithms (EAs) are often used in order to solve MOO problems, especially when the user is concerned with the trade-offs involved between the multiple objectives. One particular form of the evolutionary algorithm is the Genetic Algorithm (GA), which is a population based algorithm that tries to mimic the process of biological natural selection. GA or any other EA is able to yield a set of non-dominated solutions in a single run. However, a downside associated with such population based algorithms is that a large fraction of the datasets evaluated in order to guide the search, are actually not represented in the final solution. This becomes an issue, especially for problems involving computationally expensive functional evaluations. For such problems, “surrogate” or “meta” models are often used to approximate the exact, but computationally expensive models. This results in a significant saving in terms of computation time. However, building a surrogate model, accurate enough in the entire decision variable space is a challenge in itself. In the present work, a methodology to perform “surrogate-assisted” MOO has been proposed. The efficiency of the proposed methodology is compared against another similar methodology for surrogate-assisted MOO. The two approaches are first tested against two mathematical test problems and the most suitable method is then applied to the chemical engineering flowsheet optimisation of a coal to ammonia process with Carbon Capture and Sequestration (CCS). The results show savings in computation time even in the most conservative case.
