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Flexible modeling and control of capacitive-deionization processes through a linear-state-space dynamic Langmuir model
npj Clean Water ( IF 11.4 ) Pub Date : 2021-01-15 , DOI: 10.1038/s41545-020-00094-y
Johan Nordstrand , Joydeep Dutta

While black-box models such as neural networks have been powerful in many applications, direct physical modeling (white box) remains crucial in many fields where experimental data are difficult or time-consuming to obtain. Here, we demonstrate with an example from desalination by capacitive deionization (CDI), how an existing physical model could be strengthened by combining a general modeling framework with physical insights (gray box). Thus, a dynamic Langmuir (DL) model is extended to a linear-state-space DL model (LDL). Results obtained show the new LDL model could incorporate general structural and operational modes, including membrane CDI and constant-current operation. The formulation removes the need for direct measurements of detailed device properties without adding model complexity, and MATLAB code for automatically implementing the model is provided in the Supplementary Information. We conclude the new LDL model is widely applicable, offering great flexibility in calibration data, and enabling prediction over general operating modes.



中文翻译:

通过线性状态空间动态朗缪尔模型对电容去离子过程进行灵活建模和控制

尽管诸如神经网络之类的黑匣子模型在许多应用中功能强大,但是直接物理建模(白匣子)在难以获得实验数据或耗时的许多领域中仍然至关重要。在这里,我们以电容去离子(CDI)脱盐为例,演示如何通过将通用建模框架与物理见解(灰色框)相结合来增强现有的物理模型。因此,动态朗缪尔(DL)模型被扩展为线性状态空间DL模型(LDL)。获得的结果表明,新的LDL模型可以结合一般的结构和操作模式,包括膜CDI和恒流操作。该公式消除了直接测量详细设备特性的需求,而无需增加模型的复杂性,补充信息中提供了用于自动实现模型的MATLAB代码。我们得出结论,新的LDL模型具有广泛的适用性,为校准数据提供了极大的灵活性,并能够在常规操作模式下进行预测。

更新日期:2021-01-16
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