369877 Integrated Dynamic Real Time Optimization and Advanced Feedback Control of Continuous Tablet Manufacturing Process

Thursday, November 20, 2014: 2:10 PM
202 (Hilton Atlanta)
Ravendra Singh1, Maitraye Sen2, Fernando Muzzio2, Marianthi Ierapetritou2 and Rohit Ramachandran2, (1)NSF Engineering Research Center for Structured Organic Particulate Systems (C-SOPS), Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ, (2)Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ

It is desired for pharmaceutical companies to satisfy the frequently changing globalized market demands, to optimize resources and inventory and to maximize the revenue such that the higher amount of capital and resources invested can be recovered with sufficient profit margin within a limited reduced effective patent life. Higher regulatory constraints and relatively inefficient QBT (Quality by Testing) based open-loop batch manufacturing also contributes towards suboptimal manufacturing. Therefore, a novel manufacturing technique based on continuous processing, integrated with dynamic real time optimization and an efficient advanced control system through which the pharmaceutical manufacturing can be optimized is required.  

Recently pharmaceutical manufacturing efforts are focused on continuous manufacturing integrated with inline/online monitoring tools (PAT). In such a system, feedback control and real time optimization can be utilized to achieve better performance and quality production with minimum cost.  Advanced control systems utilizing Model Predictive Control (MPC) and classical control structures (PID) have been shown to exhibit many advantages compared to MPC or PID alone [1-2]. In such a hybrid control scheme, MPC associated with complex process dynamics, is placed at a supervisory level and provides the set points to the regulatory level PID controllers.

In this work, Dynamic Real Time Optimization (DRTO) has been integrated with a hybrid control system for a continuous tablet manufacturing process for optimal automatic feedback control and QbD-based efficient continuous manufacturing. In the proposed optimal manufacturing approach, the integrated DRTO provides the optimal operational set points for the plant control system in real time optimizing an objective function. The DRTO takes into consideration the capital fixed cost, the operating cost, the market fluctuations, the product inventory/deficiency, the product quality assurance strategy, the regulatory constraints, and the product rejections. The control system then makes sure to deliver the required product with desired quality with minimum resources and time. The overall profitability depends on the combined performance of DRTO and control system. Therefore, a robust optimization strategy and an efficient control system have been integrated for maximum return of revenues. DRTO integrated with hybrid control strategy ensures the maximum possible profit irrespective of the market demand fluctuations. A systematic generic framework including the methods and tools through which the cost and profit can be estimated and the DRTO can be integrated with hybrid control system has been developed. A DRTO and control system integration algorithm, ontological data base, model library, and supporting tools (system identification toolbox, simulation tool, optimization tool, MPC toolbox) are the integral part of the framework. The algorithm provides the step-wise guidelines for DRTO-hybrid control system integration. The database provides the cost analysis data needed to perform optimization and has been build based on extensive literature and industrial survey while the model library consisting of the process flowsheet model is used to simulate the process in closed-loop scenario. The process models have been developed in simulation tool utilizing input-output step response data and system identification toolbox with which the classical control system and MPC (designed using MPC toolbox) has been integrated. On top of control architecture, the DRTO is then integrated using optimization toolbox for real time optimization and control. Finally, the real time optimization and control framework is integrated with the continuous tablet manufacturing pilot plant through real time monitoring tools, control hardware and software and OPC communication protocol. The application of the integrated DRTO and hybrid control framework has been demonstrated through a flowsheet simulation of continuous tablet manufacturing process.

The objective of this presentation is two-fold: first to highlight the integration of DRTO with advanced hybrid control architecture, and second to demonstrate the performance of the integrated system.

 [1]. Singh, R., Sahay, A., Muzzio, F., Ierapetritou, M., Ramachandran, R. (2014). Computers & Chemical Engineering, DOI: 10.1016/j.compchemeng.2014.02.029.

[2]. Singh, R., Ierapetritou, M., Ramachandran, R. (2013). European Journal of Pharmaceutics and Biopharmaceutics, 85(3), Part B, 1164-1182.

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