(697c) Data Accuracy for Smart Manufacturing in Continuous Pharmaceutical Systems
- Conference: AIChE Annual Meeting
- Year: 2018
- Proceeding: 2018 AIChE Annual Meeting
- Group: Pharmaceutical Discovery, Development and Manufacturing Forum
Thursday, November 1, 2018 - 4:10pm-4:30pm
Data reconciliation (DR) and gross error detection (GED) are systems engineering tools for further improving the accuracy and consistency of process data through systematic data rectification and checking . DR can be posed as a constrained optimization problem, which has at its objective providing the best estimate of process variables, which satisfy the process model, including material and energy balances. Under DR random errors and gross errors associated with measurements are rectified through a model-based approach providing the sensor network has the necessary redundancy. Reliable state estimates of process variables not only enhances process monitoring capabilities but also improves the performance of control systems.
DR is a powerful tool for continuous processes whose application in industries such as oil and gas is well known. It is a powerful tool for leveraging large datasets for improving monitoring consistency in the pharmaceutical industry. In this paper, we demonstrate improved accuracy in real-time monitoring of relevant process variables in a continuous dry granulation tableting line. The dry granulation process exhibits nonlinear response and fast dynamics and hence poses interesting challenges for monitoring and control. In this paper, an earlier simulation study highlighting the application of DR for a partial dry granulation line  is extended to investigate experimental performance using the entire tableting line. The DR is conducted using a steady-state model in order to achieve fast computational times. The DR framework is experimentally demonstrated using real time measurements of the relevant CQA's on the continuous tableting pilot plant at Purdue University.
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 S. Ganesh, M. Moreno, J. Liu, M. Gonzalez, Z. Nagy, G. Reklaitis. Sensor Network for Continuous Tablet Manufacturing, Proceedings of 13th International Symposium on Process Systems Engineering, 2018 (accepted)