Abstract
Purpose
Continuous manufacturing offers shorter processing times and increased product quality assurance, among several other advantages. This makes it an ever-growing interest among pharmaceutical companies. A suitable efficient control system is however desired for continuous pharmaceutical manufacturing to achieve a consistent predefined end product quality.
Methods
In order to control product quality more accurately, the effects of input disturbances need to be proactively mitigated. Therefore, it is desired that a combined feedforward/feedback control system integrated with suitable process analytical technology (PAT) be implemented over a traditional feedback-only control system. The feedforward controller measures and takes corrective actions for disturbances proactively before they affect the process and thereby product quality. The feedback controller considers the real-time deviation of control variable from a pre-specified set point and keeps it at a minimum possible value. The deviation of a control variable from the set point could be due to both measurable and unmeasurable disturbances.
Results
In this work, a combined control strategy has been developed for a continuous twin screw wet granulation (WG) process. An integrated flowsheet model was developed and simulated in order to evaluate the effect of control loops on critical quality attributes (CQAs). Different strategies of manipulation were evaluated and the best strategy was identified.
Conclusions
In silico study on the combined feedforward/feedback control strategy and feedback-only control strategy demonstrates that the combined loop results in diminished variability of the CQAs.
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Funding
This work is supported by Glaxo Smith Kline (GSK) and the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems, through Grant NSF-ECC 0540855.
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Appendices
Appendix
Feedforward Model
The feedforward controller model was developed in MATLAB workspace and Simulink. The integrated flowsheet model developed in gPROMS was converted to an integrated flowsheet model in Simulink. Transfer function models were developed from data generated by the gPROMS model. These models were developed in System Identification Toolbox and the best fit model was selected. The pole-zero plot and bode diagrams for these transfer functions were also developed to ensure that the transfer functions were stable. These transfer function models describe the various unit operations in the flowsheet. The flowsheet transfer function model is described in Fig. 36.
Integrated flowsheet model simulated in Simulink (open loop)
The above transfer function model was simplified because during implementation a simplified model for the entire process would be required. The simplified process model which also represents the ideal case was developed using the data generated by the Simulink model described in Fig. 36. The feedforward controller was then developed using the disturbance model and the process model given in Fig. 37. The feedforward controller transfer function is achieved by equating the characteristic equation to zero. The general form of that is given in Eq. 10. Both the disturbance transfer function (Gd) and process transfer function (Gp) are specific to a particular process and material and would change if any changes are made to the process or the materials.
Integrated flowsheet with combined feedforward/feedback control in Simulink
Appendix B: Nomenclature
Abbreviations | |
APAP | Acetyl-para-aminophenol |
API | Active pharmaceutical ingredient |
CPM | Continuous pharmaceutical manufacturing |
CPP | Critical process parameter |
CQA | Critical quality attribute |
CSTR | Continuously stirred tank reactor |
CU | Content uniformity |
D2R | Duration to reject |
HPMC | Hypromellose |
IAE | Integral of absolute error |
ISE | Integral of square of error |
ITAE | Integral of time absolute error |
LOD | Loss on drying |
L/S | Liquid to solid |
M2P | Magnitude to product |
MgSt | Magnesium stearate |
MPC | Model predictive control |
MRT | Mean residence time |
MSC | Multiplicative scattering correction |
NaStGly | Sodium starch glycolate |
NIR | Near infrared |
PAT | Process analytical technology |
PFR | Plug flow reactor |
PID | Proportional integral derivative |
PLS | Partial least squares |
QbD | Quality by design |
RMSE | Root mean square error |
RMSEP | Root mean square error of prediction |
RSEP | Relative standard error of prediction |
RSD | Relative standard deviation |
RTD | Residence time distribution |
SMCC | Silicified microcrystalline cellulose |
SP | Set point |
SSE | Sum of squared errors |
T2P | Time to product |
TSG | Twin screw granulator |
WG | Wet granulation |
Symbol | Variable |
Gd(s) | Disturbance transfer function model |
Gp(s) | Process transfer function model |
Gc | Controller transfer function model |
P | Proportional gain |
I | Integral time constant |
D | Derivative time constant |
Subscript | Description |
d | Disturbance |
p | Process |
c | Controller |
1,2,3,4 | Process or controller numbers |
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Pereira, G.C., Muddu, S.V., Román-Ospino, A.D. et al. Combined Feedforward/Feedback Control of an Integrated Continuous Granulation Process. J Pharm Innov 14, 259–285 (2019). https://doi.org/10.1007/s12247-018-9347-8
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DOI: https://doi.org/10.1007/s12247-018-9347-8