Model-Predictive Design, Control, and Optimization - Pharmaceutical Technology

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Model-Predictive Design, Control, and Optimization
Applying model-predictive methods and a continuous process-control framework to a continuous tablet-manufacturing process. This article contains bonus online-exclusive material.


Pharmaceutical Technology
Volume 37, Issue 6, pp. 40-41, 77

Flowsheet modeling

The continuous manufacturing of drugs can be achieved using various routes: direct compaction (DC), roller compaction (dry granulation or DG), or wet granulation (WG), depending on the starting and desired end properties of the formulation. DC is the simplest of the processes mentioned above while DG and WG improve flowability characteristics to prevent ingredient segregation and to increase density. DC (14), DG (15), and WG (16) routes have been explored using model-predictive flowsheet methodologies. Application of advanced modeling techniques for optimization and control (17, 18) on the overall flowsheet instead of the individual unit operations would enable efficient operation of the continuous process.

The challenges associated with developing robust and reliable flowsheet models for solids' processes include:

  • Characterization of all unit operations
  • Development of models that describe their constituent mechanisms
  • Performance of experimental studies for the data acquisition of multi-dimensional key particle properties
  • Identification of all the possible manipulated and controlled variables and their interactions (17, 18)
  • Integration of process design and control to identify globally valid operating conditions.

Extensive research is ongoing to identify and develop predictive models for all the unit operations involved in the continuous tablet-manufacturing process. For integrating the various unit operations into a flowsheet, it is crucial to correctly identify the critical connecting properties that communicate across units (17). Simulating the overall flowsheet, the variations in the key properties can also be tracked during the transient states involving process start-up, perturbation propagation, dynamic response to change in settings due to control actions, and process shutdown. Furthermore, through the implementation of various operating scenarios, the flowsheet model can be used for the assessment of different process alternatives (so far achieved by expensive laboratory tests), which are then scaled up to the desired plant size. The developed and validated flowsheet-simulation system can also be used for operator training, since any sequences in operating schedules can be performed virtually and analyzed through a computer screen. Using information obtained from the flowsheet models for plant implementation is the next challenge. Incorporating control systems in the actual plant is a crucial task needed for efficient operation and minimal variation from the setpoint values.


Figure 1: Flexible continuous tablet manufacturing process with (1) direct compaction, (2) roller compaction, and (3) wet granulation. (ALL FIGURES COURTESY OF AUTHORS)
Of particular interest from a regulatory perspective is the use of integrated flowsheet models to enable identification of the propagation of noise or upsets in a particular unit operation through the entire continuous line (16, 18). This issue is directly relevant to the assessment of robustness and reliability of the continuous manufacturing system. Process optimization can be achieved by implementing optimization algorithms on the overall integrated model. Figure 1 shows a flowsheet model (simulated in gPROMS, Process Systems Enterprise) of a flexible, continuous, tablet-manufacturing process together with the implemented control system.

Model-predictive control

Various control systems can be implemented on the flowsheet model in the form of simple PID loops (15) or with advanced model-predictive control (MPC) (17). Control loops can be implemented by identifying the control-loop pairings and assessing the need for MPC in each control loop (as opposed to using just PID loops). With this information, the PID controllers are designed and implemented to obtain a predictive model of the plant, thereby suggesting the design of the MPC controller. The designed MPC is then incorporated into a general model for model-based performance evaluation.


Figure 2: Implementation of a model-predictive controller via a process analytical technology (PAT) datamanagement system (1. SynTQ, Optimal), (2. MATLAB OPC, MathWorks), or (3. SiPAT, PCS7, Siemens).
As an example, consider integration of control hardware and software in the continuous feeder and blender system, as shown in Figure 2. A PAT system is used to read the near infrared (NIR) spectral data at the blender outlet and communicate it to the multivariable analysis (MVA) model performing principle-component analysis and partial least-squares (PCA/PLS) to provide the API concentration and relative standard deviation (RSD) value. These critical quality attributes (CQAs) are used as inputs to the MPC in the process-control system. The MPC uses the two CQA inputs to drive the feed ratio and the blender speed. The MPC output (feed ratio) gives the feeders' flowrate setpoints, which are then tracked by slave PID controllers. The implemented control scheme utilises a PAT data-management system (synTQ, Optimal) to integrate a digital automation system (DeltaV, Emerson Process Management) with an NIR analyser and MVA model.

Integrating multiple system parts presented several challenges. A framework, however, is now in place that allows implementation of control architectures for a wide variety of continuous powder processes.

Acknowledgements

This work is supported by the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems, through Grant NSF-ECC 0540855.

The authors are from the Engineering Research Center for Structured Organic Particulate Systems (ERC-SOPS), Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.


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