(746e) Steady-State Data Reconciliation of a Direct Compression Tableting Line

Moreno, M., Purdue University
Liu, J., Purdue University
Ganesh, S., Purdue University
Su, Q., Purdue University
Yazdanpanah, N., U.S. Food and Drug Administration
O'Connor, T., U.S. Food and Drug Administration
Laird, C., Purdue University
Nagy, Z. K., Purdue University
Reklaitis, G., Purdue University
The assurance of critical quality attributes (CQA’s) is a major concern in the pharmaceutical manufacturing industry. During traditional batch production, the CQA’s of a batch are controlled by statistical sampling and batch rejection if sampling indicates deviations from target specifications, leading to significant waste and increased cost. Continuous manufacturing has been pursued by the pharmaceutical industry and encouraged by regulators to overcome these limitations [1]. Effective continuous manufacturing is generally implemented using a real-time process management strategy, which encompasses multiple components. The first foundational component consists of real-time measurement and monitoring of the system using Process Analytical Technology (PAT) tools [2]. The second involves a robust process control system. The third involves the detection, diagnosis and mitigation of exceptional events. In pharmaceutical manufacturing, a fourth component is also required, namely active procedures for tracking and isolating noncompliant product.

The objective of this work is to develop a model-based framework, which for a given sensor network configuration and measurement uncertainties, will predict the most likely state of the process. As a consequence, real time process decisions, whether in control or exceptional events management can be based on the most reliable estimate of the process state. The presentation focuses on real time measurement and monitoring of the direct compression continuous tableting line. Specifically, we report on the use of data reconciliation and gross error detection (GED) as tools for mitigating the effects of measurement errors and sensor malfunctions.

Data reconciliation (DR) has been widely used in other industries (i.e. oil and gas) to accomplish this goal. Data reconciliation is model based and thus consists of mechanistic (e.g., material balance) as well as empirical relationships between variables including properties [3]. Through DR, we can deal with random errors and have an estimation of unmeasured variables. However, in order to use steady-state data reconciliation (SSDR), redundancy with no gross error in the measurement is required. In order to detect gross errors in the system, we need to apply the global test and measurement test. These tests are similar to outlier detection [4]. DR also offers an opportunity to detect gross errors.

In this paper, we develop and demonstrate a DR framework for a direct compression (DC) line in real-time at steady-state. The DC line sensor network includes the on-line use of load cells for the weight of the feeders, the near-infrared (NIR) spectroscopy sensors for the powder blend composition, the X-ray sensor for the total powder flowrate and a soft-sensor model for the tablet hardness and weight. Steady-state data reconciliation (SSDR) is one of the simplest forms and offers short computational times. The results are compared to the results from the multivariate models (MVM), which are also use for the prediction of process states and error detection [5]. Since the DC line model at steady-state is linear, the results are similar [6]. The SSDR and MVM are implemented in MATLAB 2015b and the data is collected in the Emerson DeltaV server. The operator can see the real-time results in the process historian view and take a decision according to the result.


[1] S. L. Lee, T. F. O. Connor, X. Yang, C. N. Cruz, L. X. Yu, and J. Woodcock, “Modernizing Pharmaceutical Manufacturing : from Batch to Continuous Production,” no. 1, pp. 1–25, 2015.

[2] M. Ierapetritou, F. Muzzio, and G. V. Reklaitis, “Perspectives on the Continuous Manufacturing of Powder-Based Pharmaceutical Processes,” AIChE J., vol. 62, no. 6, pp. 1846–1862, 2016.

[3] S. Narasimhan and C. Jordache, Data reconciliation and Gross Error detection. Houston, TX.: Gulf Publishing Company, 2000.

[4] C. Knopf, Introduction to Data Reconciliation and Gross Error Detection. .

[5] J. A. Romagnoli and S. M.C., Data Processing and Reconciliation for Chemical Process Operations. San Diego, CA, 200AD.

[6] O. Cencic and R. Fruhwirth, “A general framework for data reconciliation-Part I: Linear constraints,” Comput. Chem. Eng., vol. 75, pp. 196–208, 2015.