Prediction and optimization of power output of single screw expander in organic Rankine cycle (ORC) for diesel engine waste heat recovery

Abstract The output characteristics of single screw expander has a direct and crucial influence on the performance of organic Rankine cycle (ORC) system. In this paper, a machine learning prediction model driven by experimental data is developed and applied to predict the power output of single screw expander. After screening different structural parameters of the model, genetic algorithm (GA) is used to optimize the initial weights and thresholds of the model, so as to further improve the generalization ability of the model. In addition, the generalization ability of the model is compared with that of the model not optimized by GA. Furthermore, the influence of operating parameters on the power output of single screw expander is analyzed by fitting algorithm in three-dimensional space. The optimization boundary value needed for prediction and optimization is determined by fitting algorithm in four-dimensional space. Finally, a prediction and optimization model is created by coupling the machine learning prediction model with GA, and the maximum power output and corresponding operating parameters of the single screw expander under full operating conditions are predicted and optimized. The results show that with the application of machine learning and GA, the maximum power output of single screw expander can be predicted and optimized precisely under full operating conditions. So as to directly guide the selection of relevant parameters in the process of theoretical analysis and experimental research.