Hydrogen production via carbonaceous catalytic methane decomposition is a complex process with simultaneous reaction, catalyst deactivation, and carbon agglomeration. Conventional reaction and deactivation models do not predict the progress of reaction accurately. Thus, statistical modeling using the method of design of experiments (DoEs) was used to design, model, and analyze experiments of methane decomposition to determine the important factors that affect the rates of reaction and deactivation. A variety of statistical models were tested in order to identify the best one agreeing with the experimental data by analysis of variance (ANOVA). Statistical regression models for initial reaction rate, catalyst activity, deactivation rate, and carbon weight gain were developed. The results showed that a quadratic model predicted the experimental findings. The main factors affecting the dynamics of the methane decomposition reaction and the catalyst deactivation rates for this process are partial pressure of methane, reaction temperature, catalytic activity, and residence time.
