This work presents the application of a Koopman operator approach to a batch pulp digester. To manufacture paper products with desired properties, it is essential to consider both macroscopic and microscopic attributes of pulp. However, the complexity of multiscale dynamics of pulping processes hinders proper control system design. Therefore, we utilize extended dynamic mode decomposition (EDMD), which is based on Koopman operator theory, to derive a global linear representation of a pulp digester. Then, we design an offset-free Koopman-based model predictive control (KMPC) system to regulate the Kappa number and cell wall thickness (CWT) of fibers at a batch pulp digester while compensating for the influence of plant-model mismatch and disturbance during operation. The numerical experiments demonstrate that the linear state-space model, obtained via EDMD, properly predicts the behavior of a batch pulp digester, and the designed offset-free KMPC system successfully drives the Kappa number and CWT to set-point values.

中文翻译：

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基于Koopman的无偏移模型预测控制在间歇浆蒸煮器中的应用

这项工作介绍了 Koopman 算子方法在间歇纸浆蒸煮器中的应用。要制造具有所需特性的纸制品，必须同时考虑纸浆的宏观和微观属性。然而，制浆过程多尺度动力学的复杂性阻碍了正确的控制系统设计。因此，我们利用基于 Koopman 算子理论的扩展动态模式分解 (EDMD) 来推导纸浆消化器的全局线性表示。然后，我们设计了一种基于 Koopman 的无偏移模型预测控制 (KMPC) 系统来调节间歇式纸浆消化器中纤维的 Kappa 数和细胞壁厚度 (CWT)，同时补偿植物模型不匹配和干扰的影响。手术。数值实验表明，线性状态空间模型，