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Steady-State Data Reconciliation Framework for a Direct Continuous Tableting Line

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Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusions

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.

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Acknowledgments

The authors will like to thank, Benjamin Rentz for his collaboration in the experimental procedure, Joonyoung Yu and Dr. Marcial Gonzalez for the design of the CDI-NIR holder, Dr. Salvador Garcia-Munoz for providing the “phi” latent variable modeling toolbox (version 1.7), Sudarshan Ganesh for his feedback in the design of experiments, and Yash D. Shah and James Wiesler for their continuous support in the project.

Funding

Funding for this project was made possible, in part, by the Food and Drug Administration through grant U01FD005535; views expressed by authors do not necessarily reflect the official policies of the Department of Health and Human Services nor does any mention of trade names, commercial practices, or organization imply endorsement by the US Government. This work was also supported in part by the National Science Foundation under grant EEC-0540855 through the Engineering Research Center for Structure Organic Particulate Systems.

Author information

Authors and Affiliations

  1. Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, 47906, USA

    Mariana Moreno, Jianfeng Liu, Qinglin Su, Cody Leach, Arun Giridhar, Zoltan K. Nagy & Gintaras V. Reklaitis

  2. Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, 20993, USA

    Nima Yazdanpanah & Thomas O’Connor

Authors
  1. Mariana Moreno
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  2. Jianfeng Liu
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  3. Qinglin Su
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  4. Cody Leach
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  5. Arun Giridhar
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  6. Nima Yazdanpanah
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  7. Thomas O’Connor
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  8. Zoltan K. Nagy
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  9. Gintaras V. Reklaitis
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Corresponding authors

Correspondence to Mariana Moreno or Gintaras V. Reklaitis.

Appendix

Appendix

Experimental Procedure and Information Flow

The experimental procedures for executing runs for cases E1 and E2 are quite straightforward. A run is initiated by turning on feeders 1 and 2, which take 1–2 min to arrive at steady state as defined by the conditions shown in Table 1. With the blender operating at 200 rpm, an additional period 2–3 min was allowed from the moment the feeders reached steady state [42] before reconciliation was initiated, corresponding to reaching a Q at or below the values in Table 1. In case E1, the process set points are as shown in Table 1. The measurements are collected and sent to MATLAB-2016a-64bit for execution, as shown in the system configuration (Appendix Fig. 10). During the runs, all variables are saved in the Emerson DeltaV v.13.3 process historian, operating using two Dell Precision T1700 64-bit computers with Windows 2008 RL2, configured as five virtual computers. The first server houses App Station (AS), ProPlus (PP), and Terminal Server 1 (TS1). The second server houses Engineering Station (ENG) and Terminal Server 2 (TS2). As configured, each virtual computer has a specific function: the ENG station is used for adding equipment and troubleshooting issues. Computer AS is used when other applications, such as MATLAB, need to communicate with the DeltaV system. The PP computer is used to add any figures in the main template for the operation of the pilot plant, process historian view diagrams, and to add or modify the control modules in DeltaV. Finally, TS1 and TS2 are used to operate the process.

The codes for the SSDR-M and the SSDR-D frameworks are implemented in MATLAB 2015b 32-bit and installed in the AS computer. Since MATLAB serves as a client, by using the OPC toolbox, the code reads the measured flows and composition from the DeltaV historian, solves the optimization problem, and writes the information directly into the DeltaV control module “SSDR.” All control modules and process historian view diagrams were created on the PP computer. For these experiments, the system was operated from the TS1 computer.

Another, physical computer: Dell Inspiron Windows 10 64-bit laptop using Intel Core™ i7 and 8 GB RAM called “PAT,” is used to connect to the necessary PAT tools, such as the CDI-NIR or the Mettler Toledo balance, and to link the required information to the other computers. All composition and flow measurements are in the DeltaV historian. However, it has to be noted, that of these four subsystem measurements, only the feeder flow values are directly sent to the DeltaV system. For the composition measurements, the CDI-NIR is connected via USB port to the “PAT” computer. By using the CDI-NIR software, called Spec32, the spectra measurement is collected as a “.spc file.” This file is generated every second and read by the multivariate statistical methods code. The code, which is in MATLAB 2016a 64-bit, executes the PLS model, created in “phi” software package and loaded as a “.mat” file. This software was acquired through personal communication with Dr. Garcia-Munoz. The results are sent to KEPServerEX v6. This server, provided by Kepware, sends the information to LinkMaster v3, which is connected to the DeltaV system. In parallel, the mass flow MATLAB code is executed using the “parpool” function. This code calls the information from the APW-Link™ software, which is connected to the Mettler Toledo balance through a COM port. It is important to mention that the acetaminophen composition and mass flow measurements are computed using an averaging time window of 10 s.

Fig. 10
figure 10

System configuration for the continuous tableting line: virtual computers (--) and physical computer (-)

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Moreno, M., Liu, J., Su, Q. et al. Steady-State Data Reconciliation Framework for a Direct Continuous Tableting Line. J Pharm Innov 14, 221–238 (2019). https://doi.org/10.1007/s12247-018-9354-9

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