Optimization strategies for simulated moving bed and PowerFeed processes

Simulated moving bed (SMB) processes have been applied to many important separations in sugar, petrochemical, and pharmaceutical industries. However, systematic optimization of SMB is still a challenging problem. Two tailored approaches are proposed, full discretization and single discretization, where both the optimal operating condition and concentration profiles are obtained by a Newton‐type solver. In a case study of fructose and glucose separation, it has been found that the full‐discretization method implemented on AMPL with IPOPT is more efficient than single‐discretization method on gPROMS with SRQPD. The reliability of the full‐discretization method is also demonstrated with case studies of a bi‐Langmuir isotherm and a PowerFeed optimization problem.