Optimal operation of simulated moving bed chromatographic processes

Optimization of startup and shutdown operation of simulated moving bed chromatographic processes

“…However, both procedures are based on the equivalent TMB model, and the "triangle theory" neglects the effect of axial dispersion and mass transfer resistances. To overcome these limitations, alternative modelbased mathematical optimization strategies have been proposed [14][15][16][17][18][19][20][21][22][23][24][25][26][27]. They consider a detailed dynamic SMB model, involve a single or multiple objective functions, and employ efficient solution techniques well developed in the mathematical programming community.…”

“…They consider a detailed dynamic SMB model, involve a single or multiple objective functions, and employ efficient solution techniques well developed in the mathematical programming community. With the ability to exploit the full process potential, they have been extensively used to optimize not only the standard SMB [14,15], but also many non-standard operations, such as VariCol [16,[19][20][21], PowerFeed [17,19,22], ModiCon [23], FF-SMB [24,25] and even the combination of different modes [18,19,26,27].…”

Neural Network-Based Identification and MPC Control of SMB Chromatography

“…If the process parameters are appropriately chosen for a given SMB equipment and a given separation task, a cyclic-steady-state (CSS), in which the process undergoes an identical transient during each switching period, is reached after a startup period. The SMB process can be described rigorously by a set of partial differential and algebraic equations (Dünnebier and Klatt, 1999). The dynamics of a single column in a SMB process are described by the general rate model in Eq.…”

“…To reduce the strong interactions of the process, the socalled ¬-factors, ¬ ½ , are used as the new system input variables for identification and control, which were introduced for the computation of optimal operating policies of the SMB-process. They are defined by a nonlinear input transformation (Dünnebier and Klatt, 1999):…”

Solving a dynamic separation problem using MINLP techniques

“…The modeling of different chromatographic separation processes has been addressed in, for example, [1] and [5]. The optimization of separation processes has been addressed in the pertinent literature, for example, in [9], [12] and in [2]. A chromatographic separation problem was modeled and solved as an MINLP problem by [6] and recently in [4].…”