Abstract
In modern industrial systems, increasing performance requirements and sustainability constraints have intensified the need for advanced optimization methodologies capable of efficiently handling complex process models. The Simulated Moving Bed (SMB) process is a well-established technology for continuous chromatographic separations, offering high productivity and reduced solvent consumption compared to batch operations. However, its optimization is challenging due to the underlying distributed-parameter nature of the process.This work presents the development of a dynamic simulation and parameter optimization framework for the SMB process, implemented in Python using the open-source CasADi framework. The SMB model accounts for axial dispersion and mass transfer using a linear driving force formulation and is discretized in space using the method of lines, resulting in a state-space representation compatible with CasADi's numerical tools. Model accuracy was validated by reproducing a benchmark case from literature and comparing concentration profiles and product purities against results obtained using MATLAB's PDEPE solver.The proposed framework was further applied to a parameter optimization problem involving the separation of fructose and glucose. The objective was to maximize the feed velocity subject to purity and recovery constraints for fructose, with the superficial velocities in each SMB section and the switching time treated as decision variables. The resulting nonlinear programming problem was solved using a direct single-shooting approach and the interior-point optimizer IPOPT. The optimized operating conditions and performance metrics closely match published results, while achieving the solution in less than 90% of the computational time reported for a gPROMS-based implementation.