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

机译:基于模型的模拟移动床色谱法分离果糖和葡萄糖的控制

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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. (C) 2002 Published by Elsevier Science Ltd. All rights reserved. [References: 35]
机译:色谱分离是一种用于分离高价值产品的扩展技术,特别是在制药,食品和精细化工领域。作为连续色谱分离过程的模拟移动床(SMB)工艺是常规批量色谱的一种有趣的替代方法,并且最近获得了越来越多的影响。 SMB过程是通过串联连接多个单个色谱柱来实现的。床的逆流运动是通过在流体流方向上周期性地切换入口和出口来实现的。由于其复杂的动态特性,SMB过程的最佳操作和自动控制是一项艰巨的任务。本文介绍了基于模型的SMB色谱分离过程优化和控制方案的设计及其在果糖和葡萄糖分离中的应用。我们提出了一种两层控制体系结构,其中通过基于严格过程模型的动态优化离线计算最佳运行轨迹。基于在线测量对模型的参数进行调整。低级控制任务是尽管存在干扰和工厂/模型不匹配,但仍将过程保持在最佳轨迹上。在此,将基于严格过程模型的仿真数据沿最优轨迹的识别模型与合适的本地控制器组合在一起。在8列SMB工厂中,果糖和葡萄糖分离的仿真研究显示了轨迹控制算法的效率。 (C)2002由Elsevier Science Ltd.出版。保留所有权利。 [参考:35]

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