Prediction of hydrogen safety parameters using intelligent techniques

With increase in the use and application of hydrogen for stationary and mobile applications, there is an increased pressure to ensure the safety handling and monitoring of this combustible gas. The associated equipment to monitor and measure explosion limit of any leakage together with the pressure and flow rate is very expensive. Any reliable mathematical or empirical means to estimate and predict those safety features of hydrogen will greatly assist in avoiding expensive instrumentation. In this paper predictive model for accurate estimation of hydrogen parameters such as percentage lower explosive limit, hydrogen pressure and hydrogen flow rate as a function of different input conditions of power supplied (voltage and current), the feed of de-ionized water and various Hogen(R)20 electrolyser system parameters is carried out. In addition, the percentage contributions of the input parameters on each hydrogen production parameters and optimum network architecture to minimize computation time and maximize network accuracy are presented. It is shown that output from the neural network predictive models of the hydrogen safety features agree well with its experimentally measured values. The hydrogen production parameters and predicted safety explosive limit were found to be less than 5% of average root mean square error. Copyright (C) 2008 John Wiley & Sons, Ltd.