Global Approach for Simulated Moving Bed Model Identification: Design of Experiments, Uncertainty Evaluation, and Optimization Strategy Assessment

Simulated moving bed (SMB) chromatography is a widely used technique for the resolution of compounds difficult to separate. SMB parameter estimation is traditionally carried out following a time and money consuming series of experiments in an SMB unit where deviations may arise. This work aims to present a novel global and straightforward parameter estimation procedure together with uncertainty analysis. Particle swarm optimization (PSO) is employed to search for parameters in an eight-dimensional space, avoid local minima, and enable uncertainty analysis. The proposed methodology is validated in a software-in-the-loop approach. A new parameter estimation is then carried out using the data of one experimental run from the literature, together with uncertainty evaluation based on the PSO-generated population that enables model validation and definition of confidence regions for the model. A robust method for the parameter estimation of an SMB unit is presented in order to produce a more precise and reliable model. In addition, it also significantly reduces the number of necessary steps for parameter estimation, leading to a more efficient procedure. The results show that it is possible to perform parameter estimation from SMB chromatography producing a more trustworthy model.