Karsten-Ulrich Klatt

Model-based optimization and control of chromatographic processes

Abstract This contribution presents an integrated approach to the optimal operation and automatic control of chromatographic separation processes in batch elution mode as well as in continuous SMB operation. The new approach is based on computationally efficient simulation models and combines techniques from mathematical optimization, parameter estimation and control theory. The resulting algorithms were implemented in an industrial standard control system and the capability of the proposed control approach is demonstrated on the separation of fructose and glucose, both in batch and SMB operation mode.

Model-based control of a simulated moving bed chromatographic process for the separation of fructose and glucose

Abstract Chromatographic separations are an expanding technology for the separation of high value products, particularly in the area of pharmaceutics, food, and fine chemicals. The simulated moving bed (SMB) process as a continuous chromatographic separation process is an interesting alternative to conventional batch chromatography, and gained more and more impact recently. The SMB process is realized by connecting several single chromatographic columns in series. A countercurrent movement of the bed is approximated by a cyclic switching of the inlet and outlet ports in the direction of the fluid stream. Because of its complex dynamics, the optimal operation and automatic control of SMB processes is a challenging task. This paper presents the design of a model-based optimization and control scheme for SMB chromatographic separation processes and its application to the separation of fructose and glucose. We propose a two-layer control architecture where the optimal operating trajectory is calculated off-line by dynamic optimization based on a rigorous process model. The parameters of the model are adapted based on online measurements. The low-level control task is to keep the process on the optimal trajectory despite disturbances and plant/model mismatch. Here identification models based on simulation data of the rigorous process model along the optimal trajectory are combined with a suitable local controller. The efficiency of the trajectory control algorithm is shown in a simulation study for the separation of fructose and glucose on an 8-column SMB plant.

Neural network-based identification of SMB chromatographic processes

Abstract In this contribution, the identification problem for the control of nonlinear SMB chromatographic processes is addressed. For process control the flow rates of extract. desorbent, and recycle of the SMB-process, and the switching time are the natural choices for the manipulated variables. However, these variables influence the process in a strongly coupled manner. Therefore, a new set of input variables is introduced by a nonlinear transformation of physical inputs, such that the couplings are reduced considerably. The front positions of the axial concentration profile are taken as model outputs. Multilayer neural networks are utilized as approximate models of the nonlinear input-output behaviour. The correlation functions between the input and output signals and the gradient distribution of the model outputs with respect to the inputs are used to determine their structural parameters. To illustrate the effectiveness of the identification method, a laboratory scale SMB process is taken as an example. The simulation results of the identified model confirm a very good approximation of the first principles models and have a satisfactory long range prediction performance.

Modeling and computationally efficient simulation of chromatographic separation processes

Chromatographic separation processes are an emerging technology, especially in the field of fine chemicals and pharmaceutical products. As an alternative to conventional batch chromatography, the simulated moving bed (SMB) process gained more and more impact recently. Because of the complex dynamics, the automatic control of chromatographic separation processes is a challenging task, the solution of which requires reliable and computationally efficient simulation models. The purpose of this contribution is to give an overview about the generation of dynamic models both for single chromatographic columns and SMB processes. We here distinguish chromatographic processes by the type of adsorption isotherm and proceed from the ideal model for chromatographic columns while increasing the model complexity as far as necessary in order to build a simulation model which on the one hand is computationally effective and on the other hand correctly describes the dynamics of the process which is relevant for on-line optimization and control purposes.

Reduced Dynamic Models of Simulated Moving Bed Chromatographic Processes

Abstract Simulated moving bed chromatographic processes are an emerging technology, especially in the field of fine chemicals and pharmaceutical products. Because of the complex process dynamics, model-based control of these processes is still an unsolved issue. The process models reported are either very simplified models which are not accurate enough for control purposes or very complex and computationally expensive. All of these models are based on first principles. The purpose of this contribution is to present newly developed reduced linear dynamic process models. The first part of the paper is devoted to an analysis of the physical properties of the system, leading to a specific structure for the reduced model and a clever choice of inputs and outputs. Based on simulation data of the complex model around the optimal operating trajectory, linear time invariant MIMO models are identified. With a rather simple model structure and low order models, a very good agreement between the predictions of the complex and of the reduced model could be achieved.

Modeling of Chromatographic Separation Processes Using Nonlinear Wave Theory

Abstract This paper presents a simplified approach for the dynamic modeling of chromatographic separation processes based on the model of the ideal chromatogaphic column, the thermodynamic equilibrium being described by the Langmuir isotherm, and nonlinear wave propagation. The approach has been applied to batch chromatography and to the SMB-(Simulated Moving Bed) process. Simulation examples for both cases are given and the limitations of the model are shown. The approach has proved to be computational efficient and to allow additional insight into the process dynamics.

Neural network-based identification of the SMB chromatographic process

Abstract In this contribution, the identification problem for the control of nonlinear SMB chromatographic processes is addressed. For process control the flow rates of extract. desorbent, and recycle of the SMB-process, and the switching time are the natural choices for the manipulated variables. However, these variables influence the process in a strongly coupled manner. Therefore, a new set of input variables is introduced by a nonlinear transformation of physical inputs, such that the couplings are reduced considerably. The front positions of the axial concentration profile are taken as model outputs. Multilayer neural networks are utilized as approximate models of the nonlinear input-output behaviour. The correlation functions between the input and output signals and the gradient distribution of the model outputs with respect to the inputs are used to determine their structural parameters. To illustrate the effectiveness of the identification method, a laboratory scale SMB process is taken as an example. The simulation results of the identified model confirm a very good approximation of the first principles models and have a satisfactory long range prediction performance.