On the Effect of Cobalt Promotion over Ni/CeO2 Catalyst for CO2 Thermal and Plasma Assisted Methanation

In recent years, carbon dioxide hydrogenation leading to synthetic fuels and value-added molecules has been proposed as a promising technology for stabilizing anthropogenic greenhouse gas emissions. Methanation or Sabatier are possible reactions to valorize the CO2. In the present work, thermal CO2 methanation and non-thermal plasma (NTP)-assisted CO2 methanation was performed over 15Ni/CeO2 promoted with 1 and 5 wt% of cobalt. The promotion effect of cobalt is proven both for plasma and thermal reaction and can mostly be linked with the basic properties of the materials.

High Performance Tunable Catalysts Prepared by Using 3D Printing

Honeycomb monoliths are the preferred supports in many industrial heterogeneous catalysis reactions, but current extrusion synthesis only allows obtaining parallel channels. Here, we demonstrate that 3D printing opens new design possibilities that outperform conventional catalysts. High performance carbon integral monoliths have been prepared with a complex network of interconnected channels and have been tested for carbon dioxide hydrogenation to methane after loading a Ni/CeO2 active phase. CO2 methanation rate is enhanced by 25% at 300 °C because the novel design forces turbulent flow into the channels network. The methodology and monoliths developed can be applied to other heterogeneous catalysis reactions, and open new synthesis options based on 3D printing to manufacture tailored heterogeneous catalysts.

Prediction of Methanol Production in a Carbon Dioxide Hydrogenation Plant Using Neural Networks

The objective of this research was to design a neural network (ANN) to predict the methanol flux at the outlet of a carbon dioxide dehydrogenation plant. For the development of the ANN, a database was generated, in the open-source simulation software “DWSIM”, from the validation of a process described in the literature. The sample consists of 133 data pairs with four inputs: reactor pressure and temperature, mass flow of carbon dioxide and hydrogen, and one output: flow of methanol. The ANN was designed using 12 neurons in the hidden layer and it was trained with the Levenberg–Marquardt algorithm. In the training, validation and testing phase, a global mean square (RMSE) value of 0.0085 and a global regression coefficient R of 0.9442 were obtained. The network was validated through an analysis of variance (ANOVA), where the p-value for all cases was greater than 0.05, which indicates that there are no significant differences between the observations and those predicted by the ANN. Therefore, the designed ANN can be used to predict the methanol flow at the exit of a dehydrogenation plant and later for the optimization of the system.