The optimal model-based design of experiments aims at designing a set of dynamic experiments yielding the most informative process data to be used for the estimation of the parameters of I first-principles dynamic process model. According to the usual procedure for parameter estimation, the experiment is first designed offline; then, the experiment is carried out in the plant, and process measurements are collected and finally. parameters are estimated after completion of the experiment. Therefore, the information gathered during the evolution of the experiment is analyzed only at the end of the experiment itself. Since the experiment is designed on the basis of the parameter estimates available before the experiment is started, the progressive increase of the information resulting from the progress of the experiment is not exploited by the designer until the end of that experiment. In this paper, a strategy for the Online model-based redesign of experiments is proposed to exploit the information as soon as it is generated from the execution of in experiment. and its performance is compared to that of a standard optimal experiment design approach. intermediate parameter estimations are carried out while the experiment is and by exploiting the information obtained, the experiment is partially redesigned before its termination. with the purpose of updating the experimental settings to generate more Valuable information for Subsequent analysis. This enables LIS to reduce the number of experimental trials that are needed to reach a statistically Sound estimation of the model parameters and results in a reduction of experimental time, raw materials needs, number of samples to be analyzed, control effort, and labor. Two simulated case studies of increasing level Of complexity are used to demonstrate the benefits ofthe proposed approach with respect to a state-of-the-art sequential niodel-based experiment design.