Multi-objective evolutionary optimization for multi-period heat exchanger network retrofit

Increase in energy efficiency and reduction in greenhouse gas (GHG) emissions in industry are important steps towards a more sustainable economy. Due to the growing share of high value-added industries, multi-period operation becomes more common in process industry. Therefore, retrofit of existing multi-period production plants is a key aspect towards more sustainable production processes. Hence, in this work, an existing two-level evolutionary algorithm using a genetic algorithm and a differential evolution for multi-period heat exchanger network retrofit is extended to consider GHG emissions as a second objective to the total annual cost (TAC). The multi-objective problem is addressed by incorporating a non-dominated sorting genetic algorithm (NSGA-II) and hypervolume indicators into the algorithm. By analyzing an industrial case study of a potato chips production, the results of the multi-objective optimization shows that GHG emissions can be reduced by 50%. However, compared to the single-objective optimization, TAC is increased by 27%. By selecting capital costs and operating costs as objectives instead, similar results to the single-objective optimization are achieved showing that the results are highly dependent on the selection of the objectives. Further, changes in utility costs and caused emissions have a high impact on the results.