Gas composition modeling in a reformed Methanol Fuel Cell system using adaptive Neuro-Fuzzy Inference Systems

摘要

This work presents a method for modeling the gas composition in a Reformed Methanol Fuel Cell system. The method is based on Adaptive Neuro-Fuzzy-Inference-Systems which are trained on experimental data. The developed models are of the H_2, CO_2, CO and CH_3OH mass flows of the reformed gas. The ANFIS models are able to predict the mass flows with mean absolute errors for the H_2 and CO_2 models of less than 1% and 6.37% for the CO model and 4.56% for the CH_3OH model.The models have a wide range of applications such as dynamic modeling, stoichiometry observation and control, advanced control algorithms, or fuel cell diagnostics systems.