Sheriff M Ziyan, Huang Yan-Shu, Bachawala Sunidhi, Gonzelez Marcial, Nagy Zoltan K, Reklaitis Gintaras V
Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA.
School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.
ESCAPE. 2022;51:1087-1092. doi: 10.1016/b978-0-323-95879-0.50182-x.
Controllers are often tuned during plant commissioning, with a fixed process model. However, over time degradation can occur in the process, the process model and the controller, making it necessary to either re-tune the controller or re-identify the process model. Authors have proposed a variety of approaches to identify plant-model mismatch (PMM) and control performance degradation (CPD). While each approach may have its own advantages and disadvantages, they are generally designed to function on different timescales. The differing timescales result in the need for a multi-level hierarchical approach to monitor, detect, and manage PMM and CPD, as illustrated through a continuous pharmaceutical manufacturing application, i.e., a direct compression tablet manufacturing process. This work also highlights the requirement for index-based metrics, that enable the impact of PMM and CPD to be quantified and assessed from a control performance monitoring perspective, to aid fault diagnosis through root cause analysis to guide maintenance decisions for continuous manufacturing applications.
控制器通常在工厂调试期间使用固定的过程模型进行调整。然而，随着时间的推移，过程、过程模型和控制器可能会出现退化，这使得有必要重新调整控制器或重新识别过程模型。作者们提出了多种方法来识别工厂模型不匹配（PMM）和控制性能退化（CPD）。虽然每种方法都有其自身的优缺点，但它们通常设计用于不同的时间尺度。不同的时间尺度导致需要一种多层次的分层方法来监测、检测和管理PMM和CPD，这通过一个连续制药制造应用（即直接压片制造过程）得到了说明。这项工作还强调了基于指标的度量的要求，这些度量能够从控制性能监测的角度量化和评估PMM和CPD的影响，以通过根本原因分析辅助故障诊断，从而指导连续制造应用的维护决策。
