Modelling the performance of an SOEC by optimization of neural network with MPSO algorithm.

This paper studies the Solid Oxide Electrolyzer Cell as a promising system in the sustainable development for the hydrogen economy and energy systems as a robust system. The Solid Oxide Electrolyzer Cell converts the steam and carbon-dioxide directly to functional fuels through consumption of the additional electrical power of green power sources or off-peak network powers. The present paper evaluates the static efficiency of the SOEC under four various gas mixtures. Modeling of this system is performed using Elman neural network (ENN) and modified particle swarm optimization (MPSO) algorithm. The MPSO algorithm is utilized to determine the optimal values for ENN adjustable parameters. It's known from the empirical results that the steam and carbon-dioxide concentrations can affect the SOEC efficiency. The operational potential and volume share of the hydrogen, carbon dioxide and steam are considered as the system inputs, and efficiency (current) is remarked as its output. The correlation factors of the achieved model are greater than 0.999, and its MSE (mean squared error) is lower than 0.017. It reveals that the forecasted values are almost equal to the empirical data. Subsequently, the efficiency of the SOEC is studied using the achieved model of the MPSO-based ENN in various feedstock concentrations. Thus, this dataset that is used for ENN model can be desirable for different applications of fast-modeling in a standalone group. It as well can be useful for cost, computing-time, and computing burden reduction in a model construction in the efficiency analyzing and system-level designing processes. • The static model of a SOEC is identified in this paper. • Elman neural network is applied in this paper as a novel method in the modeling process. • MPSO algorithm is used to increase the accuracy of the obtained model. • Static efficiency of the SOEC is evaluated for various gas mixtures. • The obtained results demonstrated the high precision of the proposed model. [ABSTRACT FROM AUTHOR]