Multi-objective performance optimization & thermodynamic analysis of solar powered supercritical CO2 power cycles using machine learning methods & genetic algorithm

The present study is focused on multi-objective performance optimization & thermodynamic analysis from the perspectives of energy and exergy for Recompression, Partial Cooling & Main Compression Intercooling supercritical CO2 (sCO2) Brayton cycles for concentrated solar power (CSP) applications using machine learning algorithms. The novelty of this work lies in the integration of artificial neural networks (ANN) and genetic algorithms (GA) for optimizing the performance of advanced sCO2 power cycles considering climatic variation, which has significant implications for both the scientific community and engineering applications in the renewable energy sector. The methodology employed includes thermodynamic analysis based on energy, exergy & environmental factors including system performance optimization. The system is modelled for net power production of 15 MW thermal output utilizing equations for the energy and exergy balance for each component. Subsequently, thermodynamic model extracted dataset used for prediction & evaluation of Random Forest, XGBoost, KNN, AdaBoost, ANN and LightGBM algorithm. Finally, considering climate conditions, multi-objective optimization is carried out for the CSP integrated sCO2 Power cycle for optimal power output, exergy destruction, thermal and exergetic efficiency. Genetic algorithm and TOPSIS (technique for order of preference by similarity to ideal solution), multi-objective decision-making tool, were used to determine the optimum operating conditions. The major findings of this work reveal significant improvements in the performance of the advanced sCO2 cycle by 1.68 % and 7.87 % compared to conventional recompression and partial cooling cycle, respectively. This research could advance renewable energy technologies, particularly concentrated solar power, by improving power cycle designs to increase system efficiency and economic feasibility. Optimized advanced supercritical CO2 power cycles in concentrated solar power plants might increase renewable energy use and energy generation infrastructure, potentially opening new research avenues.