Advanced model-based experiment design techniques are a reliable tool for rapid development and refining of process models, in particular in the area of chemical kinetics. The high parameter correlations, which are typical of the most common reaction networks (parallel, consecutive reactions, etc.), often make the identification and proper estimation of the model parameters extremely difficult. For this reason, a novel approach to model-based experiment design able to yield optimally informative experiments while simultaneously reducing the correlations between the model parameters was proposed in a previous publication(1) and was demonstrated to be highly effective. This article presents the application of this novel anti-correlation approach to the identification of a kinetic model for biodiesel transesterification from rapeseed oil, using isothermal experiments. The actual, as opposed to simulated, execution of laboratory experiments provides a further and much more significant demonstration of the effectiveness of our innovative design method. By following the suggested algorithm, three experiments were optimized for each of the two design iterations, providing statistically validated estimates for the six kinetic constants involved in the model with, on average, a medium correlation level.