Moreno Mariana, Liu Jianfeng, Su Qinglin, Leach Cody, Giridhar Arun, Yazdanpanah Nima, O'Connor Thomas, Nagy Zoltan K, Reklaitis Gintaras V
Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA.
Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD 20993, USA.
J Pharm Innov. 2019 Sep;14(3):221-238. doi: 10.1007/s12247-018-9354-9.
Reliable process monitoring in real-time remains a challenge for the pharmaceutical industry. Dealing with random and gross errors in the process measurements in a systematic way is a potential solution. In this paper, we present a process model-based framework, which for given sensor network and measurement uncertainties will predict the most likely state of the process. Thus, real-time process decisions, whether for process control or exceptional events management, can be based on the most reliable estimate of the process state.
Reliable process monitoring is achieved by using data reconciliation (DR) and gross error detection (GED) to mitigate the effects of random measurement errors and non-random sensor malfunctions. Steady-state data reconciliation (SSDR) is the simplest forms of DR but offers the benefits of short computational times. We also compare and contrast the model-based DR approach (SSDR-M) to the purely data-driven approach (SSDR-D) based on the use of principal component constructions.
We report the results of studies on a pilot plant-scale continuous direct compression-based tableting line at steady-state in two subsystems. If the process is linear or mildly nonlinear, SSDR-M and SSDR-D give comparable results for the variables estimation and GED. SSDR-M also complies with mass balances and estimate unmeasured variables.
SSDR successfully estimates the true state of the process in presence of gross errors, as long as steady state is maintained and the redundancy requirement is met. Gross errors are also detected while using SSDR-M or SSDR-D. Process monitoring is more reliable while using the SSDR framework.
实时可靠的过程监测对制药行业来说仍是一项挑战。以系统的方式处理过程测量中的随机误差和重大误差是一种潜在的解决方案。在本文中，我们提出了一个基于过程模型的框架，对于给定的传感器网络和测量不确定性，该框架将预测过程的最可能状态。因此，无论是用于过程控制还是异常事件管理的实时过程决策，都可以基于对过程状态的最可靠估计。
通过使用数据调和（DR）和重大误差检测（GED）来减轻随机测量误差和非随机传感器故障的影响，从而实现可靠的过程监测。稳态数据调和（SSDR）是DR最简单的形式，但具有计算时间短的优点。我们还基于主成分构造的使用，将基于模型的DR方法（SSDR-M）与纯数据驱动方法（SSDR-D）进行了比较和对比。
我们报告了在两个子系统中对中试规模的基于连续直接压片的制粒生产线在稳态下进行研究的结果。如果过程是线性或轻度非线性的，SSDR-M和SSDR-D在变量估计和GED方面给出了可比的结果。SSDR-M还符合质量平衡并估计未测量的变量。
只要保持稳态并满足冗余要求，SSDR就能在存在重大误差的情况下成功估计过程的真实状态。在使用SSDR-M或SSDR-D时也能检测到重大误差。使用SSDR框架时，过程监测更可靠。
