A comprehensive approach for the multi-objective optimization of Heat Recovery Steam Generators to maximize cost-effectiveness and output power. (June 2021)

• Record Type: Journal Article
• Authors: Bianco, Nicola; Fragnito, Andrea; Iasiello, Marcello; Maria Mauro, Gerardo
• Is Part Of: Sustainable energy technologies and assessments. Volume 45(2021)
• Publication Date: 2021-06
• Subjects: Thermodynamics -- Energy efficiency -- Multi-objective optimization -- Genetic algorithm -- Heat recovery steam generator; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2021.101162
• Languages: English
• ISSNs: 2213-1388

Abstract

Graphical abstract: Highlights: Optimizing Heat Recovery Steam Generators (HRSG) is crucial for multi-generation. A comprehensive approach using genetic algorithm & sensitivity analysis is proposed. Pareto optimization is applied to maximize output power and minimize global cost. Geometry and thermodynamic variables are optimized. Economic savings up to 20% are achieved limiting the HRSG size. Abstract: 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.