Machine learning-based multi-objective optimization and thermal assessment of supercritical CO2 Rankine cycles for gas turbine waste heat recovery

Technologies for utilizing waste heat for power generation have attracted significant attention in recent years due to their potential to enhance energy efficiency and reduce greenhouse gas emissions. This research focuses on the comparative and optimization analysis of three supercritical carbon dioxide (sCO₂) Rankine cycles (simple, cascade, and split) for gas turbine waste heat recuperation. The study begins with parametric analysis, investigating the significant effects of key variables, including turbine inlet temperature, condenser inlet temperature, and pinch point temperature, on the thermal performance of advanced sCO₂ power cycles. To identify the most efficient cycle configuration, a multi-objective optimization approach is employed. This approach combines a Genetic Algorithm with machine learning regression models (Random Forest, XGBoost, Artificial Neural Network, Ridge Regression, and K-Nearest Neighbors) to predict cycle performance using a dataset extracted from cycle simulations. The decision-making process for determining the optimal cycle configuration is facilitated by the TOPSIS (technique for order of preference by similarity to the ideal solution) method. The study's major findings reveal that the split cycle outperforms the simple and cascade configurations in terms of power generation across various operating conditions. The optimized split cycle not only demonstrates superior power output but also exhibits enhanced net power output, heat recovery, system and exergy efficiency of 7.99 MW, 76.17 %, 26.86 % and 57.96 %, respectively, making it a promising choice for waste heat recovery applications. This research has the potential to contribute to the advancement and widespread adoption of waste heat recovery in energy technologies boosting system efficiency and economic feasibility. It provides a new perspective for future research, contributing to the improvement of energy generation infrastructure.