Artificial neural networks with response surface methodology for optimization of selective CO2 hydrogenation using K-promoted iron catalyst in a microchannel reactor
School of Engineering
CO2 hydrogenation was optimized by a combination of AANs (Artificial Neuron Networks) with RSM (Response Surface Methodology) in a microchannel reactor using a K-promoted iron-based catalyst. This robust and cost-effective methodology was reliable to extensively analyze the effect of operating conditions i.e. gas ratio, temperature, pressure, and space velocity on product distribution of selective CO2 hydrogenation. With experimental data as training data using ANNs and Box-Behnken design as design of experiment, the obtained model was able to present good results in a nonlinear noisy process with significant changes of critical operation parameters in an experimental design plan during CO2 hydrogenation using K-promoted iron-based catalyst in a microchannel reactor. The achieved quadratic model was flexible and effective in optimizing either single or multiple objections of product distribution for CO2 hydrogenation.