Large scale nonlinear optimization for asymmetric operation and design of Simulated Moving Beds

Simulated Moving Bed (SMB) was developed as a realization of continuous countercurrent operation of chromatographic separation. An SMB unit consists of several columns of the same length connected in series, where feed and desorbent are supplied and extract and raffinate are withdrawn continuously. This operation is repeated by shifting the supply/withdrawal points at a regular interval, making the operation symmetric. In this study, we explore asymmetric operation and design through a full-cycle optimization model, where the operation of the entire cycle is described within a nonlinear programming (NLP) problem and the Partial Differential Algebraic Equations (PDAEs) are fully discretized both in temporal and spatial domains. The NLP problem is implemented within the AMPL modeling environment and is solved using IPOPT, an interior-point NLP solver. We found that this problem is solved efficiently, and introducing a full-cycle formulation has the potential to improve the performance of SMB, as shown through single and multi-objective optimization studies.