A comparative study of metaheuristic algorithms for thermoeconomic optimization of an Organic Rankine Cycle

The efficient recovery of waste heat from biogas power plants via Organic Rankine Cycles (ORC) is essential for sustainable energy but requires satisfying conflicting thermodynamic and economic objectives. This study addresses the need for robust optimization strategies by evaluating nine recent metaheuristic algorithms to identify the most effective methods for maximizing net power while minimizing system costs. A multi-objective thermoeconomic model utilizing the specific exergy costing method was developed for a toluene-based ORC and optimized using algorithms including Dung Beetle, Snake, and NSGA-II. The optimization reduced the unit electricity cost by 9.1% (from $0.088/kWh to $0.080/kWh), with the Dung Beetle algorithm exhibiting superior convergence (Hypervolume 25.90) and the Snake algorithm demonstrating the best solution uniformity (Spread 1.54) with computational speed (0.084 s). These findings underscore that selecting algorithms based on specific exploration-exploitation characteristics is critical for achieving cost-effective and thermodynamically efficient power generation designs.