In this work, an RTD based digital twin has been developed for continuous pharmaceutical manufacturing process. The heart of the digital twin is a modular integrated flowsheet model capable of predicting the mixing in each unit operation as well as in an integrated line. The flowsheet model consists of the mathematical RTD models coupled with mechanistic models. The model library and knowledge base are two important supporting tools for the developed digital twin. The model library consists of the RTD model of basic elements such as plug flow process, perfectly mixed process or continuous stirred tank (CST), and dead zone (DZ). Subsequently, via combining these basic elements, the pharmaceutical unit operation (e.g. blender, feed frame) RTD model library has been developed. The knowledge base which has been developed through extensive experimentations consist of the RTD parameters for a broad range of equipmentâs and materials. The digital twin of any processes can be easily generated using these tools via drag and drop approach. The digital twin is flexible in nature, can be adapted for any continuous pharmaceutical manufacturing plant and thus should have a broad range of real time applications through distributed control system (DCS) as well as for offline research and development purposes.
The applications of the digital twin to predict the mixing capabilities and effect of disturbances have been demonstrated. The applications of the RTD based digital twin for real time diversion of tablets have been demonstrated to assure the content uniformity of the tablets. In CM, the drug concentration is measured in real time before the tablet compaction (chute &/or feed frame) using PAT sensor. The proposed control strategy then uses this inlet concentration to determine a signal for the diversion strategy that can accurately be used to reject tablets that are out of tolerance limits at the outlet of the tablet press. Two strategies, i.e. âfixed window based strategyâ and RTD based strategy have been developed, compared and evaluated. In fixed window approach, the tablet diversion is facilitated through knowledge of time delays from the point of detection to the point of the affect (tablet press outlet gate) in the system. The sensor that detects the concentration is connected to a comparator block which decides if the said concentration is within the specifications. If it is not within specification, the experimentally derived time delay is applied and post this the diversion begins. The diversion stops when a concentration within spec is detected and the another time delay is applied. In RTD based approach, the RTD is used to predict the outlet concentration from the inlet concentration. The predicted signal is then used to initiate the diversion. The first approach is simpler to implement but may lead to lower production efficiency. The second approach is based on more advanced RTD based technique and will ensure more efficiency but is relatively complex to implement. The applications of the RTD based digital twin for material traceability has been also demonstrated. A corresponding software prototype has been also developed to automate the material traceability procedure [3].
The objective of this presentation is to folds. First to highlight the development of the RTD based digital twin and then to demonstrate its applications for prediction of mixing, assurance of content uniformity via real time tablet diversion, and materials traceability.
References
[1). Singh, R. (2019). Systematic framework for implementation of RTD based control system into continuous pharmaceutical manufacturing pilot-plant. Pharma. Issue 34, 43-46.
[2]. Bhaskar, A., Singh, R. (2018). Residence time distribution (RTD) based control system for continuous pharmaceutical manufacturing process. Journal of Pharmaceutical Innovation. DOI: 10.1007/s12247-018-9356-7.
[3]. Billups, M., Singh, R. (2018). Systematic framework for implementation of material traceability into continuous pharmaceutical tablet manufacturing process. Journal of Pharmaceutical Innovation. https://doi.org/10.1007/s12247-018-9362-9.