Global Approach for Simulated Moving Bed Model Identification: Design of Experiments, Uncertainty Evaluation, and Optimization Strategy Assessment
Rodrigo V. A. Santos, Anderson Prudente, Ana M. Ribeiro, Alírio E. Rodrigues, José M. Loureiro, Márcio A.F. Martins, Karen Valverde Pontes, Idelfonso B. R. Nogueira
Abstract
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.