Study of optimization and prediction methods for PEMEC performance considering the effects of multiple operating parameters

Abstract

The influence of operating parameters on the hydrogen production performance of the proton exchange membrane electrolysis cell (PEMEC) is crucial. In this paper, we first investigate the effect of a single operating parameter on the performance of PEMEC, and find the law of single operation effect. After that, the orthogonal experimental methods are used to investigate the effect of cross-coupling of combined operating parameters on the performance of PEMEC and the optimal combination of levels of operating parameters is obtained. Finally, the LSTM neural network is used to predict the PEMEC performance under the multiple operating parameters. The results showed that the weights of voltage, temperature, water flow rate, and bolt torque on PEMEC performance were 68.72 %, 20.63 %, 6.17 %, and 4.48 %, respectively. The optimal combination of operating parameters for the experiment is a torque of 3.5 N m, temperature of 70 °C, voltage of 2.0 V, and the water flow rate of 40 mL/min. It is also found that the long short term mermory (LSTM) neural network is able to predict the performance of PEMEC well, and that the experimental data-driven approach is an effective method for predicting the experimental performance.