Bayesian experimental design is a tool for guiding experiments founded on the principle of expected information gain. I.e., which experiment design will inform the most about the model can be predicted before experiments in a laboratory are conducted. Plausible synthetic data allows for mapping out the expected information gain of the experimental design space. The chemical kinetics of a membrane reactor are modeled to demonstrate the design of experiments algorithm. The design space consists of reactor volume and reactor feed gas temperature. A steepest ascent algorithm is implemented for locating the greatest expected information gain from experiments.