A comprehensive approach for the multi-objective optimization of Heat Recovery Steam Generators to maximize cost-effectiveness and output power

Environmental problems have offered new challenges to the energy production sector, starting from the need of increasing the efficiency of thermodynamic systems to preserve fuel consumption. Accordingly, multi-energy generation systems are among the most efficient generation systems, and Heat Recovery Steam Generators (HRSGs) represent key components of such systems. In this regard, this paper shows a comprehensive approach to optimize a HRSG by using a multi-objective Genetic Algorithm (GA). The aim is to highlight how to determine the optimal values of geometrical and thermodynamic design variables according to two objective functions: the global costs to be minimized and the steam turbine output power to be maximized. Starting from a reference HRSG design, thermodynamic modeling and simulations as well as the optimization procedure are performed in MATLAB®. A validation is carried out to show the accuracy of the modeling approach. Latin Hypercube Sampling is applied to create a uniform sample to select the design variables based on a global sensitivity analysis, producing a significant reduction of computational efforts. Then, the GA optimization is performed to achieve the Pareto front, collecting the best trade-off design solutions. Economic savings up to 20% are achieved limiting the HRSG size.