## 1 Introduction
Minimizing this source of error implies high computational times to get reliable results. Conversely, the implementation of artificial intelligence techniques [20; 21] such as ANNs, overlooks them. SOFCs fast and reliable simulation is of interest of a bunch of different applications. First, at component level, they are called in 3D-CFD models, to improve significantly computational times and to approach the problem of transient simulation [25]. Second, at system level, a consistent simulation tool is necessary to forecast the SOFC performance when the system asks for operating conditions modification, to further optimize its working point and to provide a prompt regulation. In this framework, in the scientific literature there are a few examples of ANNs applications to SOFC simulation. In References [26; 27] the problem of internal multiple reforming is studied, in References [28; 29] the complex composition of the fuel is taken up, while in Reference [30] fault diagnosis in SOFC is deeply investigated. In detail, in Reference [28], the effect of anode and cathode feeding composition on the polarization curve of a SOFC is addressed. In that paper, the ANN algorithm is developed on the experimental evidence from test benches described in References [31; 32] and shows both good simulation performances and a lean architecture (after the first hidden layer, a linear transfer function is applied). Yet, its applicability is valid for SOFC operation at 800 \({}^{\circ}\)(common parameter among all the tests used for the training/validation procedure) and to a rather limited fuel composition range. In addition, the problem of internal reforming of methane is not considered. Moreover, the original input parameters come from two different works and there is no mention about the anode materials used for the experiments. The material and the cell geometry determine electric and catalytic properties of the SOFC, hence they are one fundamental to assess the applicability of an ANN simulator. An improved ANN model for SOFC performance simulation is presented in Reference [26], where three levels of operative temperature (700-750-800 \({}^{\circ}\)C) are accounted for. However, since the paper aims at investigating multiple reforming mechanisms occurring in direct-biogas fuel cells, only the operation on humidified CH4/CO2 gas mixtures is taken into account. Whilst that study provides thorough insight on complex competitive reactions (steam and dry reforming), its applicability is limited to a narrow range of fuels and does not consider other gas species. Regarding some promising SOFC applications, the fuel gas (hydrocarbon reformate, coal and biomass syngas) has non-negligible amounts of H2 and CO. This substantially modifies the reaction rates associated to methane decomposition. A slightly more complex fuel mixture (28.1% CH4, 56.7% H2O, 0.5% CO, 12% H2 and 2.7% CO2) is considered in Reference [27]. Nonetheless, in that paper, gas components fractions do not vary in the data-set used to develop the ANN. Again, the results obtained do not show a high generalization capability with regard to any operative conditions. For the sake of generalization and the reduction of computational time, an ANN simulator based on a broader application framework and state-of-the-art SOFC materials is the topic addressed in this work. Hence, this simulator is able to handle variable temperature conditions and a wider range of anode feeding compositions, considering the co-occurrence of CH4, H2, CO and CO2 and it predicts voltage losses from a few input parameters (temperature, fuel main components partial flow rates, electric load). Such algorithm is suitable to calculate quickly the SOFC electric power output in a wide range of applications, resulting flexible for many system integration designs, which show promising potentialities (e.g., biogas-fed small-scale SOFC units [33], integrated-gasifier fuel cell systems [34] etc.).