Experimental study and artificial neural network based prediction of a free piston expander-linear generator for small scale organic Rankine cycle

Abstract

In this paper, a novel free piston expander-linear generator (FPE-LG) prototype has been developed for small scale organic Rankine cycle system. The effects of three key operating parameters, including intake pressure, operation frequency and external load resistance on piston dynamics, output characteristics of the linear generator, and system energy conversion efficiency are investigated. An artificial neural network (ANN) based prediction model is established after evaluating different learning rates, hidden layer neural numbers and train functions. The ANN model is also validated and tested using the experimental data with consideration of mean squared error and correlation coefficient. Finally, combined the genetic algorithm with the ANN model, a parametric optimization and performance prediction for maximum power output of the linear generator is conducted. The results show that the free piston assembly operates stably with good consistency. Higher intake pressure and external load resistance are beneficial for improving the piston dynamics and output characteristics of the linear generator while the optimal operation frequency corresponding to the maximum peak power output is more dependent on the coordinated variation of the operating parameters. The maximum system energy conversion efficiency can reach up to 28.81% with the intake pressure of 0.2 MPa, operation frequency of 1.5 Hz and external load resistance of 5 Ω. The proposed ANN model shows a strong learning ability and generalization performance. The correlation coefficients between the ANN predictions and experimental data obtained from the validation and test processes are all close to 1. The optimized peak power output can reach up to 100.47 W based on the proposed ANN model. The ANN based method can provide a useful guidance for the performance prediction and coordinated optimization with the least deviation and high accuracy.