This study explored the dehydrogenation of n-butane using Pd, Rh, and Ru catalysts on aluminum and silicon oxide supports. Aluminum oxide (Al2O3) showed superior conversion of n-butane, especially with 1% Pd/γAl2O3, yielding 31% target alkenes. This catalyst offers potential for scaling up n-butane dehydrogenation technology.
Impact factors, that are shaping the individualistic perception of the supported objects by the relevant subjects, who interact with them, directly or indirectly, are considered in this research. A form of impact factors’ (performing impact on the supported software complexes) representation has been studied and proposed.
Model based on colored Petri nets, and dedicated for analysis an impact factors of the software complexes support automation processes, has been developed. Model provides possibilities for simulation of the processes of impact factors analysis in the field of software complexes support automation when solving the scientific and applied task of analyzing and restoring the boundaries of impact factors in supported objects subjective perception models with encapsulated artificial neural networks of multilayer perceptron type.
This study demonstrates the effect of operating conditions (Red-GHSV, inlet H2/CO, Oprat-GHSV) and the effect of Fe-Co-Ce nanocatalyst support. A statistical model using the response surface methodology (RSM) was applied with the target of achieving higher olefins selectivity in Fischer-Tropsch synthesis, which indicates the interaction effects of factors. The conditions under which three objectives optimization for maximizing olefins and minimizing paraffins and methane were determined.