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PLS data-driven based approach to design of a simulated moving bed process

机译：基于PLS数据驱动的模拟移动床过程设计方法

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摘要

Chromatographic processes provide a powerful tool for the separation of mixtures, in which the components have different absorption affinities. In this area, the simulated moving bed (SMB) technology is becoming an important technique for large-scale continuous chromatographic separation processes and its applications are found in the pharmaceutical industry and in the production of fine chemicals. The successful design and operation of SMB units depend on the correct choice of operating conditions, particularly of the flow rate in each zone. A detailed and reliable dynamic model of the process is necessary for proper design; however, due to the complex dynamics of the process, the choice of operating condition is not straightforward. In this work, the iterative learning design based on partial least squares (PLS) approach for SMB process is proposed. PLS is a data-driven approach that extracts the essential information of the process state from operation data through mathematical and statistical methods. It facilitates data-compression by condensing the variance of the process into a very low-dimensional latent subspace. Moreover, the MIMO SMB process can be transformed into a system of the parallel uni-variate SISO design problem. Also, the incorporated dEWMA strategy acts as a model updating method to reflect process decay or drift. Based on the dEWMA PLS model, two design strategies are proposed: cycle-to-cycle and within cycle design. The performance of these design strategies is assessed by implementation on a virtual eight column SMB unit.
机译：色谱法为分离混合物提供了强大的工具，其中的组分具有不同的吸收亲和力。在该领域，模拟移动床（SMB）技术正成为大规模连续色谱分离过程的重要技术，其应用已在制药行业和精细化学品的生产中找到。 SMB装置的成功设计和运行取决于正确选择的运行条件，尤其是每个区域的流量。正确设计过程的详细而可靠的动态模型是必需的;但是，由于过程的复杂性，操作条件的选择并不容易。在这项工作中，提出了基于偏最小二乘（PLS）方法的SMB迭代学习设计。 PLS是一种数据驱动的方法，可通过数学和统计方法从操作数据中提取过程状态的基本信息。它通过将过程的方差压缩到一个非常低维的潜在子空间中来促进数据压缩。此外，MIMO SMB过程可以转换为并行单变量SISO设计问题的系统。而且，合并的dEWMA策略充当模型更新方法，以反映过程衰减或漂移。基于dEWMA PLS模型，提出了两种设计策略：逐周期和周期内设计。通过在虚拟的八列SMB单元上实施，可以评估这些设计策略的性能。

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