论文标题
同时模型拓扑筛选，参数估计和最佳实验数量识别的嵌套环：应用于模拟移动床单元
A nested loop for simultaneous model topology screening, parameters estimation, and identification of the optimal number of experiments: Application to a Simulated Moving Bed unit
论文作者
论文摘要
模拟移动床（SMB）色谱法是一种众所周知的技术，用于分辨出几种高价值添加的化合物。当一个人处理复杂的系统（例如模拟移动的床单）时，参数识别和模型拓扑定义非常艰巨。此外，必要的大量实验可能是一个膨胀的过程。因此，这项工作提出了一种用于参数估计的新方法，筛选模型sink-source的最合适拓扑（由吸附等速度方程定义），并定义了识别模型所需的最小实验数量。因此，考虑到工作的三个主要目标：参数估计，提出了一个嵌套的循环优化问题；通过等温定义筛选拓扑筛选；产生精确模型所需的最小实验数量。所提出的方法通过在数据中引入噪声并使用循环中的软件（SIL）方法来模拟实际情况。数据核对和不确定性评估为参数估计增加了鲁棒性，从而为模型增加了精度和可靠性。考虑到文献中的实验数据，除了在交叉验证之后应用于参数估计的样本外，该方法已被验证。结果证实，可以直接从具有最小的系统知识的SMB单元直接进行可信赖的参数估计。
Simulated Moving Bed (SMB) chromatography is a well-known technique for the resolution of several high-value-added compounds. Parameters identification and model topology definition are arduous when one is dealing with complex systems such as a Simulated Moving Bed unit. Moreover, the large number of experiments necessary might be an expansive-long process. Hence, this work proposes a novel methodology for parameter estimation, screening the most suitable topology of the models sink-source (defined by the adsorption isotherm equation) and defining the minimum number of experiments necessary to identify the model. Therefore, a nested loop optimization problem is proposed with three levels considering the three main goals of the work: parameters estimation; topology screening by isotherm definition; minimum number of experiments necessary to yield a precise model. The proposed methodology emulated a real scenario by introducing noise in the data and using a Software-in-the-Loop (SIL) approach. Data reconciliation and uncertainty evaluation add robustness to the parameter estimation adding precision and reliability to the model. The methodology is validated considering experimental data from literature apart from the samples applied for parameter estimation, following a cross-validation. The results corroborate that it is possible to carry out trustworthy parameter estimation directly from an SMB unit with minimal system knowledge.
