Artificial Neural Network for Combined Steam-Carbon Dioxide Reforming of Methane.

The CH₄ conversion, CO₂ conversion, and H₂/CO ratio were set as dependent variables, as the feed rate, flow rate and reaction temperature as independent variables in the complex reaction of methane. We used the Artificial Neural Network (ANN) technique to build a model of the process. The ANN technique was able to predict the reforming process with higher accuracy due to the training capability.

Modeling of Complex Reforming Reaction of Methane by Response Surface Methodology.

The objective of this study was to investigate the effect of feed composition, space velocity, and reaction temperature on methane/carbon dioxide conversion and H₂/CO ratio. Independent variables were feed ratio, flow rate, and reaction temperature while CH₄ conversion, CO₂ conversion, and H₂/CO ratio were set as dependent variables in the complex reaction of methane. As a result of mixed reforming reaction of methane through RSM modeling method, the reaction temperature was found to have the greatest influence.

Effect of Tungsten Addition on Nickel-Based Catalyst for Combined Steam and CO2 Reforming of Methane.

In this study, we investigated the effect of tungsten addition on Ni-based catalyst in methane steam-CO₂ reforming. All the catalysts were prepared by the co-impregnation method with varied tungsten loading. The as-prepared catalysts were characterized by inductively coupled plasma atomic emission spectrometer, X-ray diffraction, temperature-programmed reduction and thermogravimetric analysis.

The Promoting Role of W in Nickel Based Catalyst for Bi-Reforming.

In the present study, co-impregnated tungsten and silver onto nickel based catalysts have been investigated. The influences caused by tungsten modification were inspected by XRD, XPS, TPR and TEM with Elemental Mapping from SEM. the result revealed similarity in terms of physicochemical properties.