Prediction of optimal operating parameters for Kalina cycle power system based on BP neural network algorithm
YANG Yue-ying
WANG Jian-yong
WANG Tao
CHEN Hai-feng
Abstract：Kalina cycle power generation technology is an important technology for low grade energy recovery and utilization,and the evaporation pressure and temperature are key parameters that affect the system performance.Obtaining optimal operating parameters under different design conditions is complicated and time-consuming due to the diversity of heat sources and ammonia concentrations.By means of genetic algorithm combined with thermodynamic model,1 705 sets of data were obtained with the goal of maximal net power output,and the prediction model of optimal operating parameter for Kalina cycle was established based on the BP neural network algorithm.The result showed that when the number of neurons in the single hidden layer was nine and the training function was trainlm,the prediction result of the neural network was the best.The prediction errors of the verification sets are all within 1.5%,which indicates that the neural network model can predict the optimal operating parameters of the Kalina cycle well.
Keywords：Kalina cycleBP neural networkprediction modeloptimal operating parameters
Publication Date：2023-11-15
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 1-5 )