A structural optimization method for maximizing power output in multi-stage self-superheated systems

This study introduces a novel structural optimization methodology for determining the optimal architecture of multi-stage power generation systems integrating steam turbines, self-superheaters, and Organic Rankine Cycles. Unlike conventional approaches that optimize parameters within predefined system configurations, this methodology systematically determines the optimal number and arrangement of system components through a three-tier nested algorithmic framework. The innovation lies in a generalized algorithm that iteratively evaluates different structural configurations, optimizing pressure distributions to identify the optimal system architecture for maximum power output. Application of the methodology yields a 4.96 % increase in power generation, accompanied by reductions in turbine outlet moisture content of 19.02 % and 17.38 %, thereby mitigating erosion risk and enhancing component lifespan. Furthermore, exergy destruction is reduced by 3.82 % and 0.85 %, reflecting improved energy utilization. The methodology's generalizability enables application across diverse thermal power systems requiring coordinated steam flow and pressure optimization, extending beyond geothermal applications to solar thermal, biomass, and hybrid renewable energy systems. Key methodological contributions are digested below:•Development of a systematic structural optimization framework using novel pseudocode for architecture determination•Introduction of coordinated multi-stage optimization balancing steam flow distribution between competing cycles•Creation of a generalized methodology applicable to diverse thermal power generation systems.