Decision support for ranking Pareto optimal process designs under uncertain market conditions

Considering the uncertainty of economic conditions, multi-objective optimisation can be favoured to single-objective optimisation for process design. However, from the Pareto sets generated by multi-objective optimisation it is not obvious to identify the best one, given that each solution is optimal with regard to the selected objectives. A method taking into account the economic parameters uncertainty to support decision making based on the Pareto-optimal solutions is proposed. It uses a Monte-Carlo simulation to define the probability of each of the Pareto optimal configuration to be in the list of the best configurations from the economical point of view. For a given economic context defined the most probable best configurations are identified. The proposed method is applied to two cases: the CO2 capture in power plants and synthetic natural gas production from biomass resources. The results allow to identify the most attractive system designs and give recommendations for the process engineers.