S. Grüner

Nonlinear control of a reactive distillation column

Abstract Control of reactive distillation columns is a challenging task due to the complex dynamics arising from the coupling of reaction and separation. In this paper, asymptotically exact input/output-linearization is applied in simulation studies to an industrial reactive distillation column which is operated by Bayer AG. The resulting control law is rather general and can be easily adopted for other reactive distillation columns. This control scheme requires knowledge of the complete state of the process and therefore an observer is designed. Asymptotically exact input/output-linearization inherits robust stability from a robust observer. It is intuitively argued that the proposed observer is robust w.r.t. both model structure and parameter errors. In order to compensate for steady state observer offsets an outer control loop with simple PI-controllers is implemented. Simulation studies evidence that in comparison with a well-tuned linear controller the nonlinear controller shows a superior performance with respect to setpoint-changes and disturbances, even in the presence of unknown input delays.

Optimal operation of simulated moving bed chromatographic processes by means of simple feedback control

In this contribution, simple methods are presented for controlling a simulated moving bed (SMB) chromatographic process with standard PI (proportional integral) controllers. The first method represents a simple and model-free inferential control scheme which was motivated from common distillation column control. The SMB unit is equipped with UV detectors. The UV signals in the four separation zones of the unit are fixed by four corresponding PI controllers calculating the ratio of liquid and solid flow in the respective separation zone. In order to be able to adjust the product purity a second, model-based control scheme is proposed. It makes use of the nonlinear wave propagation phenomena in the apparatus. The controlled chromatographic unit is automatically working with minimum solvent consumption and maximum feed throughput--without any numerical optimization calculations. This control algorithm can therefore also be applied for fast optimization of SMB processes.

The Structures of eIF4E-eIF4G Complexes Reveal an Extended Interface to Regulate Translation Initiation.

Eukaryotic initiation factor 4G (eIF4G) plays a central role in translation initiation through its interactions with the cap-binding protein eIF4E. This interaction is a major drug target for repressing translation and is naturally regulated by 4E-binding proteins (4E-BPs). 4E-BPs and eIF4G compete for binding to the eIF4E dorsal surface via a shared canonical 4E-binding motif, but also contain auxiliary eIF4E-binding sequences, which were assumed to contact non-overlapping eIF4E surfaces. However, it is unknown how metazoan eIF4G auxiliary sequences bind eIF4E. Here, we describe crystal structures of human and Drosophila melanogaster eIF4E-eIF4G complexes, which unexpectedly reveal that the eIF4G auxiliary sequences bind to the lateral surface of eIF4E, using a similar mode to that of 4E-BPs. Our studies provide a molecular model of the eIF4E-eIF4G complex, shed light on the competition mechanism of 4E-BPs, and enable the rational design of selective eIF4G inhibitors to dampen dysregulated translation in disease.

Nonlinear model predictive control of multicomponent distillation columns using wave models

Abstract A novel control concept for multicomponent distillation columns is presented. The concept is based on nonlinear wave-propagation phenomena that occur in counter-current separation processes. On this basis a reduced order model has been developed in previous work that not only considers profile positions but also the profile shape itself. The reduced model gives direct access to key parameters of the plant, such as the separation front positions. Furthermore, it allows realtime computations for multicomponent distillation columns. Such a model is used for both, the nonlinear model predictive control (NMPC) and the observer design. The observer uses temperature measurements and gives estimated temperature and concentration profile positions as well as compositions in the product streams. The robustness of the observer is shown intuitively and in simulation studies. The control of multicomponent distillation is formulated within the NMPC framework by penalising the deviation of the front positions from their reference points and ensuring the product specifications by means of constraints. By directly taking account of product specifications the presented control concept differs from inferential control schemes known from literature. Due to the fact that the concept is based on simple temperature measurements an industrial application seems easily possible.

Nonlinear Analysis of gPROMS Models Using DIVA as via a CAPE ESO interface

Abstract The CAPE ESO interface of the process simulator gPROMS is used to pass model information to numerical methods contained in the simulation environment DIVA. By the interface, algorithms for the continuation of stable and unstable steady state and periodic solutions can be applied directly to gPROMS models. The use of the interface is illustrated by a detailed nonlinear model of an industrial reactive distillation column.

Nonlinear control of an industrial reactive distillation column

Abstract Control of reactive distillation columns is a challenging task due to the complex dynamics arising from the coupling of reaction and separation. In this paper asymptotically exact input/output-linearization is applied to an industrial reactive distillation column. This control scheme requires knowledge of the complete state of the process and therefore an observer is designed. To compensate for steady state observer offsets an outer control loop with simple PI-controllers is implemented. In comparison to a well tuned linear controller the nonlinear controller shows a superior performance with respect to setpoint-changes and disturbances, even in the presence of unknown input delays.