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.
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.
Figure 1: Flexible continuous tablet manufacturing process with (1) direct compaction, (2) roller compaction, and (3) wet
granulation. (ALL FIGURES COURTESY OF AUTHORS)
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.
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.
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).
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.
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.