Modeling of thermal expansion coefficient of perovskite oxide for solid oxide fuel cell cathode

Artificial intelligence models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the material science. This study investigates the applicability of adaptive neuro-fuzzy inference system (ANFIS) approach for modeling the performance parameters of thermal expansion coefficient (TEC) of perovskite oxide for solid oxide fuel cell cathode. Oxides (Ln = La, Nd, Sm and M = Fe, Ni, Mn) have been prepared and characterized to study the influence of the different cations on TEC. Experimental results have shown TEC decreases favorably with substitution of Nd3+ and Mn3+ ions in the lattice. Structural parameters of compounds have been determined by X-ray diffraction, and field emission scanning electron microscopy has been used for the morphological study. Comparison results indicated that the ANFIS technique could be employed successfully in modeling thermal expansion coefficient of perovskite oxide for solid oxide fuel cell cathode, and considerable savings in terms of cost and time could be obtained by using ANFIS technique.