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Supporting the shift towards continuous pharmaceutical manufacturing by condition monitoring

机译:通过状态监测支持向连续制药生产的转变

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Over the last decade there has been an increased interest in the pharmaceutical industry to shift the manufacturing process of drugs from batch to continuous operation. The continuous manufacturing of pharmaceuticals provides significant benefits, e.g. savings in cost, time and materials - to name but a few. The implementation of a continuous manufacturing strategy, however, is challenging. To gain profit from a continuous process one has to ensure its proper operation, i.e. a long time-span until the next prospective unscheduled downtime. Thus, the installed operation units have to be: 1) robust against disturbances by engineering design principles and by advanced fault tolerant control schemes, respectively; and 2) the condition of the operation units has to be monitored reliably to trigger, in case of need, appropriate intervention strategies in a timely manner. In this paper, the focus is on the monitoring aspect. Here, a model-based fault detection and identification framework is implemented, which selects the most data-supported model candidate from a set of predefined model hypotheses including the reference model (normal behavior) as well as failure models. In addition, to enable an improved diagnosis the system under study can be steered deliberately based on the proposed concept resulting into an active fault diagnosis framework. Preliminary results are demonstrated by an academic three-tank system.
机译:在过去的十年中,制药行业越来越关注将药品的生产过程从批处理转变为连续操作。药物的连续生产提供了显着的好处,例如节省成本,时间和材料-仅举几例。然而,实施连续制造策略具有挑战性。为了从连续过程中获利,必须确保其正常运行,即较长时间直到下一次潜在的计划外停机。因此,所安装的操作单元必须:1)分别通过工程设计原则和先进的容错控制方案来抵抗干扰。 2)必须对操作单元的状况进行可靠的监控,以在需要时及时触发适当的干预策略。在本文中,重点是监视方面。在这里,实现了基于模型的故障检测和识别框架,该框架从一组包括参考模型(正常行为)以及故障模型的预定义模型假设中选择数据支持程度最高的候选模型。另外,为了实现改进的诊断,可以根据提出的概念有意地操纵所研究的系统,从而形成一个主动的故障诊断框架。初步结果由一个学术的三缸系统证明。

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