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,…