Machine learning-based multi-objective optimization and thermal assessment of supercritical CO_(2) 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(sCO_(2))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 sCO_(2) 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.

Multi-objective performance optimization&thermodynamic analysis of solar powered supercritical CO_(2)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 CO_(2)(sCO_(2))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 sCO_(2)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 sCO_(2)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 sCO_(2)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 CO_(2)power cycles in concentrated solar power plants might increase renewable energy use and energy generation infrastructure,potentially opening new research avenues.