A new approach for the modelling of the simultaneous dry and steam reforming of CH4 (methane multiple-reforming (MMR)) within the Ni-YSZ anode of a solid oxide fuel cell (SOFC) is introduced in this paper. MMR is modelled by using artificial neural network (ANN) and fuzzy inference system (FIS) that can express the gas composition and temperature dependences of the consumption or the production rate of gaseous species involved in MMR. The necessary parameters for this approach are determined from the measured reforming kinetics for an anode-supported cell (ASC) fuelled by a CH4-CO2-H2O-N-2 mixture. The developed MMR model is incorporated into a 3D-CFD planar ASC model to calculate the SOFC performance, and the calculated results match well with experimental values for the feed of simulated biogas (CH4/CO2 = 1) and H-2. The established SOFC model considering MMR is a powerful tool to simulate the performance of internal reforming SOFC. (C) 2017 Elsevier B.V. All rights reserved.