Abstract
The volumetric gas-liquid mass transfer coefficient (kLa) is a key performance parameter in stirred tank reactors and is commonly determined through extensive experiments across the operational space. This work presents an automatic, closed-loop framework for kLa determination based on model-based design of experiments (MBDoE), in which agitation and aeration inputs are adapted in real time.During each experiment, dissolved oxygen data is collected and used to estimate the parameters of a Van't Riet kLa relation. The parameter uncertainty is quantified using the covariance matrix, and the experiments are iteratively selected based on D-optimality or E-optimality MBDoE, until a threshold of RSEi < 0.15 is reached for all parameters. The MBDoE approach is evaluated through repeated runs and compared against random designs, full factorial (FF) design, and a full grid design.The results demonstrate that the closed-loop MBDoE framework can significantly reduce the number of experiments required to characterize kLa while maintaining accurate predictions of gas-liquid mass transfer over the design space. D-optimal MBDoE converges in fewer than four experiments on average, while E-optimal MBDoE converges in less than six experiments on average, translating into approximately a 60% reduction of the number of experiments as compared to a full factorial DoE design.